diff --git a/notebooks/benchmark.ipynb b/notebooks/benchmark.ipynb
index b87cf36..ef41f09 100644
--- a/notebooks/benchmark.ipynb
+++ b/notebooks/benchmark.ipynb
@@ -17,7 +17,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -29,7 +29,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -39,13 +39,13 @@
     "from sklearn.model_selection import train_test_split\n",
     "from sklearn import tree\n",
     "from sklearn.metrics import classification_report, confusion_matrix, f1_score\n",
-    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier\n",
+    "from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, BaggingClassifier\n",
     "from stree import Stree"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -64,13 +64,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 4,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "2020-06-15 10:17:17\n"
+     "text": "2020-06-27 23:33:07\n"
     }
    ],
    "source": [
@@ -86,7 +88,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -98,8 +100,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "execution_count": 6,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "output_type": "stream",
@@ -114,7 +118,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -126,8 +130,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "execution_count": 8,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "output_type": "stream",
@@ -151,7 +157,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,7 +168,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -172,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -182,7 +188,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -192,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -202,12 +208,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Gradient Boosting\n",
-    "gradient = GradientBoostingClassifier(random_state=random_state)"
+    "# Bagging\n",
+    "bagging = BaggingClassifier(random_state=random_state)"
    ]
   },
   {
@@ -219,7 +225,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -244,20 +250,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
+   "execution_count": 16,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "************************** Linear Tree **********************\nTrain Model Linear Tree took: 13.91 seconds\n=========== Linear Tree - Train 199,364 samples =============\n              precision    recall  f1-score   support\n\n           0   1.000000  1.000000  1.000000    199020\n           1   1.000000  1.000000  1.000000       344\n\n    accuracy                       1.000000    199364\n   macro avg   1.000000  1.000000  1.000000    199364\nweighted avg   1.000000  1.000000  1.000000    199364\n\n=========== Linear Tree - Test 85,443 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999578  0.999613  0.999596     85295\n           1   0.772414  0.756757  0.764505       148\n\n    accuracy                       0.999192     85443\n   macro avg   0.885996  0.878185  0.882050     85443\nweighted avg   0.999184  0.999192  0.999188     85443\n\nConfusion Matrix in Train\n[[199020      0]\n [     0    344]]\nConfusion Matrix in Test\n[[85262    33]\n [   36   112]]\n************************** Random Forest **********************\nTrain Model Random Forest took: 173.1 seconds\n=========== Random Forest - Train 199,364 samples =============\n              precision    recall  f1-score   support\n\n           0   1.000000  1.000000  1.000000    199020\n           1   1.000000  1.000000  1.000000       344\n\n    accuracy                       1.000000    199364\n   macro avg   1.000000  1.000000  1.000000    199364\nweighted avg   1.000000  1.000000  1.000000    199364\n\n=========== Random Forest - Test 85,443 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999660  0.999965  0.999812     85295\n           1   0.975410  0.804054  0.881481       148\n\n    accuracy                       0.999625     85443\n   macro avg   0.987535  0.902009  0.940647     85443\nweighted avg   0.999618  0.999625  0.999607     85443\n\nConfusion Matrix in Train\n[[199020      0]\n [     0    344]]\nConfusion Matrix in Test\n[[85292     3]\n [   29   119]]\n************************** Stree (SVM Tree) **********************\nTrain Model Stree (SVM Tree) took: 38.4 seconds\n=========== Stree (SVM Tree) - Train 199,364 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999623  0.999864  0.999744    199020\n           1   0.908784  0.781977  0.840625       344\n\n    accuracy                       0.999488    199364\n   macro avg   0.954204  0.890921  0.920184    199364\nweighted avg   0.999467  0.999488  0.999469    199364\n\n=========== Stree (SVM Tree) - Test 85,443 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999637  0.999918  0.999777     85295\n           1   0.943548  0.790541  0.860294       148\n\n    accuracy                       0.999555     85443\n   macro avg   0.971593  0.895229  0.930036     85443\nweighted avg   0.999540  0.999555  0.999536     85443\n\nConfusion Matrix in Train\n[[198993     27]\n [    75    269]]\nConfusion Matrix in Test\n[[85288     7]\n [   31   117]]\n************************** AdaBoost model **********************\nTrain Model AdaBoost model took: 47.21 seconds\n=========== AdaBoost model - Train 199,364 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999392  0.999678  0.999535    199020\n           1   0.777003  0.648256  0.706815       344\n\n    accuracy                       0.999072    199364\n   macro avg   0.888198  0.823967  0.853175    199364\nweighted avg   0.999008  0.999072  0.999030    199364\n\n=========== AdaBoost model - Test 85,443 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999484  0.999707  0.999596     85295\n           1   0.806202  0.702703  0.750903       148\n\n    accuracy                       0.999192     85443\n   macro avg   0.902843  0.851205  0.875249     85443\nweighted avg   0.999149  0.999192  0.999165     85443\n\nConfusion Matrix in Train\n[[198956     64]\n [   121    223]]\nConfusion Matrix in Test\n[[85270    25]\n [   44   104]]\n"
+     "text": "************************** Linear Tree **********************\nTrain Model Linear Tree took: 14.43 seconds\n=========== Linear Tree - Train 199,364 samples =============\n              precision    recall  f1-score   support\n\n           0   1.000000  1.000000  1.000000    199020\n           1   1.000000  1.000000  1.000000       344\n\n    accuracy                       1.000000    199364\n   macro avg   1.000000  1.000000  1.000000    199364\nweighted avg   1.000000  1.000000  1.000000    199364\n\n=========== Linear Tree - Test 85,443 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999578  0.999613  0.999596     85295\n           1   0.772414  0.756757  0.764505       148\n\n    accuracy                       0.999192     85443\n   macro avg   0.885996  0.878185  0.882050     85443\nweighted avg   0.999184  0.999192  0.999188     85443\n\nConfusion Matrix in Train\n[[199020      0]\n [     0    344]]\nConfusion Matrix in Test\n[[85262    33]\n [   36   112]]\n************************** Random Forest **********************\nTrain Model Random Forest took: 165.2 seconds\n=========== Random Forest - Train 199,364 samples =============\n              precision    recall  f1-score   support\n\n           0   1.000000  1.000000  1.000000    199020\n           1   1.000000  1.000000  1.000000       344\n\n    accuracy                       1.000000    199364\n   macro avg   1.000000  1.000000  1.000000    199364\nweighted avg   1.000000  1.000000  1.000000    199364\n\n=========== Random Forest - Test 85,443 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999660  0.999965  0.999812     85295\n           1   0.975410  0.804054  0.881481       148\n\n    accuracy                       0.999625     85443\n   macro avg   0.987535  0.902009  0.940647     85443\nweighted avg   0.999618  0.999625  0.999607     85443\n\nConfusion Matrix in Train\n[[199020      0]\n [     0    344]]\nConfusion Matrix in Test\n[[85292     3]\n [   29   119]]\n************************** Stree (SVM Tree) **********************\nTrain Model Stree (SVM Tree) took: 37.53 seconds\n=========== Stree (SVM Tree) - Train 199,364 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999623  0.999864  0.999744    199020\n           1   0.908784  0.781977  0.840625       344\n\n    accuracy                       0.999488    199364\n   macro avg   0.954204  0.890921  0.920184    199364\nweighted avg   0.999467  0.999488  0.999469    199364\n\n=========== Stree (SVM Tree) - Test 85,443 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999637  0.999918  0.999777     85295\n           1   0.943548  0.790541  0.860294       148\n\n    accuracy                       0.999555     85443\n   macro avg   0.971593  0.895229  0.930036     85443\nweighted avg   0.999540  0.999555  0.999536     85443\n\nConfusion Matrix in Train\n[[198993     27]\n [    75    269]]\nConfusion Matrix in Test\n[[85288     7]\n [   31   117]]\n************************** AdaBoost model **********************\nTrain Model AdaBoost model took: 46.98 seconds\n=========== AdaBoost model - Train 199,364 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999392  0.999678  0.999535    199020\n           1   0.777003  0.648256  0.706815       344\n\n    accuracy                       0.999072    199364\n   macro avg   0.888198  0.823967  0.853175    199364\nweighted avg   0.999008  0.999072  0.999030    199364\n\n=========== AdaBoost model - Test 85,443 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999484  0.999707  0.999596     85295\n           1   0.806202  0.702703  0.750903       148\n\n    accuracy                       0.999192     85443\n   macro avg   0.902843  0.851205  0.875249     85443\nweighted avg   0.999149  0.999192  0.999165     85443\n\nConfusion Matrix in Train\n[[198956     64]\n [   121    223]]\nConfusion Matrix in Test\n[[85270    25]\n [   44   104]]\n************************** Bagging model **********************\nTrain Model Bagging model took: 77.93 seconds\n=========== Bagging model - Train 199,364 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999864  1.000000  0.999932    199020\n           1   1.000000  0.921512  0.959153       344\n\n    accuracy                       0.999865    199364\n   macro avg   0.999932  0.960756  0.979542    199364\nweighted avg   0.999865  0.999865  0.999862    199364\n\n=========== Bagging model - Test 85,443 samples =============\n              precision    recall  f1-score   support\n\n           0   0.999637  0.999953  0.999795     85295\n           1   0.966942  0.790541  0.869888       148\n\n    accuracy                       0.999590     85443\n   macro avg   0.983289  0.895247  0.934842     85443\nweighted avg   0.999580  0.999590  0.999570     85443\n\nConfusion Matrix in Train\n[[199020      0]\n [    27    317]]\nConfusion Matrix in Test\n[[85291     4]\n [   31   117]]\n"
     }
    ],
    "source": [
     "# Train & Test models\n",
     "models = {\n",
     "    'Linear Tree':linear_tree, 'Random Forest': random_forest, 'Stree (SVM Tree)': stree,  \n",
-    "    'AdaBoost model': adaboost\n",
+    "    'AdaBoost model': adaboost, 'Bagging model': bagging\n",
     "}\n",
     "\n",
     "best_f1 = 0\n",
@@ -273,13 +281,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
+   "execution_count": 17,
+   "metadata": {
+    "tags": []
+   },
    "outputs": [
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "**************************************************************************************************************\n*The best f1 model is Random Forest, with a f1 score: 0.8815 in 173.095 seconds with 0.7 samples in train dataset\n**************************************************************************************************************\nModel: Linear Tree\t Time:  13.91 seconds\t f1: 0.7645\nModel: Random Forest\t Time: 173.09 seconds\t f1: 0.8815\nModel: Stree (SVM Tree)\t Time:  38.40 seconds\t f1: 0.8603\nModel: AdaBoost model\t Time:  47.21 seconds\t f1: 0.7509\n"
+     "text": "**************************************************************************************************************\n*The best f1 model is Random Forest, with a f1 score: 0.8815 in 165.193 seconds with 0.7 samples in train dataset\n**************************************************************************************************************\nModel: Linear Tree\t Time:  14.43 seconds\t f1: 0.7645\nModel: Random Forest\t Time: 165.19 seconds\t f1: 0.8815\nModel: Stree (SVM Tree)\t Time:  37.53 seconds\t f1: 0.8603\nModel: AdaBoost model\t Time:  46.98 seconds\t f1: 0.7509\nModel: Bagging model\t Time:  77.93 seconds\t f1: 0.8699\n"
     }
    ],
    "source": [
@@ -314,10 +324,11 @@
     "******************************************************************************************************************\n",
     "Model: Linear Tree       Time:  23.05 seconds\t f1: 0.7645\n",
     "Model: Random Forest\t Time: 218.97 seconds\t f1: 0.8815\n",
-    "Model: Stree (SVM Tree)\t Time:  49.45 seconds\t f1: 0.8467\n",
+    "Model: Stree (SVM Tree)\t Time:  49.45 seconds\t f1: 0.8603\n",
     "Model: AdaBoost model\t Time:  73.83 seconds\t f1: 0.7509\n",
-    "Model: Gradient Boost.\t Time: 388.69 seconds\t f1: 0.5259\n",
     "Model: Neural Network\t Time:  25.47 seconds\t f1: 0.8328\n",
+    "Model: Bagging model\t Time:  77.93 seconds\t f1: 0.8699\n",
+    "\n",
     "```"
    ]
   }
diff --git a/notebooks/features.ipynb b/notebooks/features.ipynb
index 811fcdd..13508af 100644
--- a/notebooks/features.ipynb
+++ b/notebooks/features.ipynb
@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Test smple_weight, kernels, C, sklearn estimator"
+    "# Test sample_weight, kernels, C, sklearn estimator"
    ]
   },
   {
@@ -68,7 +68,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (5492, 28)  y.shape (5492,)\nFraud: 9.068% 498\nValid: 90.932% 4994\n[0.09131113 0.09131113 0.09131113 0.09131113] [0.08919903 0.08919903 0.08919903 0.08919903]\n"
+     "text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (5492, 28)  y.shape (5492,)\nFraud: 9.086% 499\nValid: 90.914% 4993\n[0.09157128 0.09157128 0.09157128 0.09157128] [0.08919903 0.08919903 0.08919903 0.08919903]\n"
     }
    ],
    "source": [
@@ -134,7 +134,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Test simple_weights\n",
+    "## Test sample_weights\n",
     "Compute accuracy with weights in samples. The weights are set based on the inverse of the number of samples of each class"
    ]
   },
@@ -148,7 +148,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "Accuracy of Train without weights 0.9901144640998959\nAccuracy of Train with    weights 0.9924557752341311\nAccuracy of Tests without weights 0.9872572815533981\nAccuracy of Tests with    weights 0.9824029126213593\n"
+     "text": "Accuracy of Train without weights 0.9875130072840791\nAccuracy of Train with    weights 0.9919354838709677\nAccuracy of Tests without weights 0.9866504854368932\nAccuracy of Tests with    weights 0.9872572815533981\n"
     }
    ],
    "source": [
@@ -177,7 +177,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "Time: 10.77s\tKernel: linear\tAccuracy_train: 0.986732570239334\tAccuracy_test: 0.9860436893203883\nTime: 1.56s\tKernel: rbf\tAccuracy_train: 0.9945369406867846\tAccuracy_test: 0.9896844660194175\nTime: 1.23s\tKernel: poly\tAccuracy_train: 0.9942767950052029\tAccuracy_test: 0.9860436893203883\n"
+     "text": "Time: 1.45s\tKernel: linear\tAccuracy_train: 0.9854318418314256\tAccuracy_test: 0.9842233009708737\nTime: 0.50s\tKernel: rbf\tAccuracy_train: 0.9940166493236212\tAccuracy_test: 0.9908980582524272\nTime: 0.42s\tKernel: poly\tAccuracy_train: 0.9945369406867846\tAccuracy_test: 0.9872572815533981\n"
     }
    ],
    "source": [
@@ -200,11 +200,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {
     "tags": []
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "output_type": "stream",
+     "name": "stdout",
+     "text": "************** C=0.001 ****************************\nClassifier's accuracy (train): 0.9826\nClassifier's accuracy (test) : 0.9854\nroot feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1664\nroot - Down, <cgaf> - Leaf class=1 belief= 0.986348 impurity=0.0269 counts=(array([0, 1]), array([  4, 289]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.982259 impurity=0.0349 counts=(array([0, 1]), array([3488,   63]))\n\n**************************************************\n************** C=0.01 ****************************\nClassifier's accuracy (train): 0.9826\nClassifier's accuracy (test) : 0.9860\nroot feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1664\nroot - Down, <cgaf> - Leaf class=1 belief= 0.986348 impurity=0.0269 counts=(array([0, 1]), array([  4, 289]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.982259 impurity=0.0349 counts=(array([0, 1]), array([3488,   63]))\n\n**************************************************\n************** C=1 ****************************\nClassifier's accuracy (train): 0.9841\nClassifier's accuracy (test) : 0.9848\nroot feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1664\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0268\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([286]))\nroot - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4938\nroot - Down - Up - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([5]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([4]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.982812 impurity=0.0338 counts=(array([0, 1]), array([3488,   61]))\n\n**************************************************\n************** C=5 ****************************\nClassifier's accuracy (train): 0.9854\nClassifier's accuracy (test) : 0.9873\nroot feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1664\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0199\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([295]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([3]))\nroot - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0316\nroot - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.5000\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.984199 impurity=0.0311 counts=(array([0, 1]), array([3488,   56]))\n\n**************************************************\n************** C=17 ****************************\nClassifier's accuracy (train): 0.9841\nClassifier's accuracy (test) : 0.9836\nroot feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1664\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0524\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([287]))\nroot - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.3200\nroot - Down - Up - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([2]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([8]))\nroot - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0349\nroot - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.2975\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([2]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([9]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.982749 impurity=0.0339 counts=(array([0, 1]), array([3475,   61]))\n\n**************************************************\n2.3880 secs\n"
+    }
+   ],
    "source": [
     "t = time.time()\n",
     "for C in (.001, .01, 1, 5, 17):\n",
@@ -228,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "metadata": {
     "tags": []
    },
@@ -236,7 +242,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1659\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0139\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([270]))\nroot - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.2311\nroot - Down - Up - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([13]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\nroot - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0375\nroot - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.5000\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\nroot - Up - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0370\nroot - Up - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.3200\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([4]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\nroot - Up - Up - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0348\nroot - Up - Up - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([3]))\nroot - Up - Up - Up - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0349\nroot - Up - Up - Up - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4688\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([10]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([6]))\nroot - Up - Up - Up - Up - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0296\nroot - Up - Up - Up - Up - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([9]))\nroot - Up - Up - Up - Up - Up - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0296\nroot - Up - Up - Up - Up - Up - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4800\nroot - Up - Up - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([3]))\nroot - Up - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.985791 impurity=0.0280 counts=(array([0, 1]), array([3469,   50]))\n"
+     "text": "root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1664\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0524\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([287]))\nroot - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.3200\nroot - Down - Up - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([2]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([8]))\nroot - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0349\nroot - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.2975\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([2]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([9]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.982749 impurity=0.0339 counts=(array([0, 1]), array([3475,   61]))\n"
     }
    ],
    "source": [
@@ -247,7 +253,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "metadata": {
     "tags": []
    },
@@ -255,7 +261,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1659\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0139\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([270]))\nroot - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.2311\nroot - Down - Up - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([13]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\nroot - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0375\nroot - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.5000\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([1]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\nroot - Up - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0370\nroot - Up - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.3200\nroot - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([4]))\nroot - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\nroot - Up - Up - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0348\nroot - Up - Up - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([3]))\nroot - Up - Up - Up - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0349\nroot - Up - Up - Up - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4688\nroot - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([10]))\nroot - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([6]))\nroot - Up - Up - Up - Up - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0296\nroot - Up - Up - Up - Up - Up - Down, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([9]))\nroot - Up - Up - Up - Up - Up - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0296\nroot - Up - Up - Up - Up - Up - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.4800\nroot - Up - Up - Up - Up - Up - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([3]))\nroot - Up - Up - Up - Up - Up - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([2]))\nroot - Up - Up - Up - Up - Up - Up - Up, <cgaf> - Leaf class=0 belief= 0.985791 impurity=0.0280 counts=(array([0, 1]), array([3469,   50]))\n"
+     "text": "root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1664\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0524\nroot - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([287]))\nroot - Down - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.3200\nroot - Down - Up - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([2]))\nroot - Down - Up - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([8]))\nroot - Up feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.0349\nroot - Up - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.2975\nroot - Up - Down - Down, <pure> - Leaf class=1 belief= 1.000000 impurity=0.0000 counts=(array([1]), array([2]))\nroot - Up - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([9]))\nroot - Up - Up, <cgaf> - Leaf class=0 belief= 0.982749 impurity=0.0339 counts=(array([0, 1]), array([3475,   61]))\n"
     }
    ],
    "source": [
@@ -273,7 +279,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {
     "tags": []
    },
@@ -281,7 +287,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "1 functools.partial(<function check_no_attributes_set_in_init at 0x11e6f3f80>, 'Stree')\n2 functools.partial(<function check_estimators_dtypes at 0x11e6ef0e0>, 'Stree')\n3 functools.partial(<function check_fit_score_takes_y at 0x11e6e9f80>, 'Stree')\n4 functools.partial(<function check_sample_weights_pandas_series at 0x11e6e68c0>, 'Stree')\n5 functools.partial(<function check_sample_weights_not_an_array at 0x11e6e69e0>, 'Stree')\n6 functools.partial(<function check_sample_weights_list at 0x11e6e6b00>, 'Stree')\n7 functools.partial(<function check_sample_weights_shape at 0x11e6e6c20>, 'Stree')\n8 functools.partial(<function check_sample_weights_invariance at 0x11e6e6d40>, 'Stree')\n9 functools.partial(<function check_estimators_fit_returns_self at 0x11e6f30e0>, 'Stree')\n10 functools.partial(<function check_estimators_fit_returns_self at 0x11e6f30e0>, 'Stree', readonly_memmap=True)\n11 functools.partial(<function check_complex_data at 0x11e6e6ef0>, 'Stree')\n12 functools.partial(<function check_dtype_object at 0x11e6e6e60>, 'Stree')\n13 functools.partial(<function check_estimators_empty_data_messages at 0x11e6ef200>, 'Stree')\n14 functools.partial(<function check_pipeline_consistency at 0x11e6e9e60>, 'Stree')\n15 functools.partial(<function check_estimators_nan_inf at 0x11e6ef320>, 'Stree')\n16 functools.partial(<function check_estimators_overwrite_params at 0x11e6f3e60>, 'Stree')\n17 functools.partial(<function check_estimator_sparse_data at 0x11e6e67a0>, 'Stree')\n18 functools.partial(<function check_estimators_pickle at 0x11e6ef560>, 'Stree')\n19 functools.partial(<function check_classifier_data_not_an_array at 0x11e6f8200>, 'Stree')\n20 functools.partial(<function check_classifiers_one_label at 0x11e6efc20>, 'Stree')\n21 functools.partial(<function check_classifiers_classes at 0x11e6f3680>, 'Stree')\n22 functools.partial(<function check_estimators_partial_fit_n_features at 0x11e6ef680>, 'Stree')\n23 functools.partial(<function check_classifiers_train at 0x11e6efd40>, 'Stree')\n24 functools.partial(<function check_classifiers_train at 0x11e6efd40>, 'Stree', readonly_memmap=True)\n25 functools.partial(<function check_classifiers_train at 0x11e6efd40>, 'Stree', readonly_memmap=True, X_dtype='float32')\n26 functools.partial(<function check_classifiers_regression_target at 0x11e6f8cb0>, 'Stree')\n27 functools.partial(<function check_supervised_y_no_nan at 0x11e6df5f0>, 'Stree')\n28 functools.partial(<function check_supervised_y_2d at 0x11e6f3320>, 'Stree')\n29 functools.partial(<function check_estimators_unfitted at 0x11e6f3200>, 'Stree')\n30 functools.partial(<function check_non_transformer_estimators_n_iter at 0x11e6f8830>, 'Stree')\n31 functools.partial(<function check_decision_proba_consistency at 0x11e6f8dd0>, 'Stree')\n32 functools.partial(<function check_fit2d_predict1d at 0x11e6e9440>, 'Stree')\n33 functools.partial(<function check_methods_subset_invariance at 0x11e6e95f0>, 'Stree')\n34 functools.partial(<function check_fit2d_1sample at 0x11e6e9710>, 'Stree')\n35 functools.partial(<function check_fit2d_1feature at 0x11e6e9830>, 'Stree')\n36 functools.partial(<function check_fit1d at 0x11e6e9950>, 'Stree')\n37 functools.partial(<function check_get_params_invariance at 0x11e6f8a70>, 'Stree')\n38 functools.partial(<function check_set_params at 0x11e6f8b90>, 'Stree')\n39 functools.partial(<function check_dict_unchanged at 0x11e6e9050>, 'Stree')\n40 functools.partial(<function check_dont_overwrite_parameters at 0x11e6e9320>, 'Stree')\n41 functools.partial(<function check_fit_idempotent at 0x11e6f8f80>, 'Stree')\n42 functools.partial(<function check_n_features_in at 0x11e6fc050>, 'Stree')\n43 functools.partial(<function check_requires_y_none at 0x11e6fc0e0>, 'Stree')\n"
+     "text": "1 functools.partial(<function check_no_attributes_set_in_init at 0x11f161ef0>, 'Stree')\n2 functools.partial(<function check_estimators_dtypes at 0x11f15e050>, 'Stree')\n3 functools.partial(<function check_fit_score_takes_y at 0x11f158ef0>, 'Stree')\n4 functools.partial(<function check_sample_weights_pandas_series at 0x11f155830>, 'Stree')\n5 functools.partial(<function check_sample_weights_not_an_array at 0x11f155950>, 'Stree')\n6 functools.partial(<function check_sample_weights_list at 0x11f155a70>, 'Stree')\n7 functools.partial(<function check_sample_weights_shape at 0x11f155b90>, 'Stree')\n8 functools.partial(<function check_sample_weights_invariance at 0x11f155cb0>, 'Stree')\n9 functools.partial(<function check_estimators_fit_returns_self at 0x11f161050>, 'Stree')\n10 functools.partial(<function check_estimators_fit_returns_self at 0x11f161050>, 'Stree', readonly_memmap=True)\n11 functools.partial(<function check_complex_data at 0x11f155e60>, 'Stree')\n12 functools.partial(<function check_dtype_object at 0x11f155dd0>, 'Stree')\n13 functools.partial(<function check_estimators_empty_data_messages at 0x11f15e170>, 'Stree')\n14 functools.partial(<function check_pipeline_consistency at 0x11f158dd0>, 'Stree')\n15 functools.partial(<function check_estimators_nan_inf at 0x11f15e290>, 'Stree')\n16 functools.partial(<function check_estimators_overwrite_params at 0x11f161dd0>, 'Stree')\n17 functools.partial(<function check_estimator_sparse_data at 0x11f155710>, 'Stree')\n18 functools.partial(<function check_estimators_pickle at 0x11f15e4d0>, 'Stree')\n19 functools.partial(<function check_classifier_data_not_an_array at 0x11f167170>, 'Stree')\n20 functools.partial(<function check_classifiers_one_label at 0x11f15eb90>, 'Stree')\n21 functools.partial(<function check_classifiers_classes at 0x11f1615f0>, 'Stree')\n22 functools.partial(<function check_estimators_partial_fit_n_features at 0x11f15e5f0>, 'Stree')\n23 functools.partial(<function check_classifiers_train at 0x11f15ecb0>, 'Stree')\n24 functools.partial(<function check_classifiers_train at 0x11f15ecb0>, 'Stree', readonly_memmap=True)\n25 functools.partial(<function check_classifiers_train at 0x11f15ecb0>, 'Stree', readonly_memmap=True, X_dtype='float32')\n26 functools.partial(<function check_classifiers_regression_target at 0x11f167c20>, 'Stree')\n27 functools.partial(<function check_supervised_y_no_nan at 0x11f14d560>, 'Stree')\n28 functools.partial(<function check_supervised_y_2d at 0x11f161290>, 'Stree')\n29 functools.partial(<function check_estimators_unfitted at 0x11f161170>, 'Stree')\n30 functools.partial(<function check_non_transformer_estimators_n_iter at 0x11f1677a0>, 'Stree')\n31 functools.partial(<function check_decision_proba_consistency at 0x11f167d40>, 'Stree')\n32 functools.partial(<function check_fit2d_predict1d at 0x11f1583b0>, 'Stree')\n33 functools.partial(<function check_methods_subset_invariance at 0x11f158560>, 'Stree')\n34 functools.partial(<function check_fit2d_1sample at 0x11f158680>, 'Stree')\n35 functools.partial(<function check_fit2d_1feature at 0x11f1587a0>, 'Stree')\n36 functools.partial(<function check_fit1d at 0x11f1588c0>, 'Stree')\n37 functools.partial(<function check_get_params_invariance at 0x11f1679e0>, 'Stree')\n38 functools.partial(<function check_set_params at 0x11f167b00>, 'Stree')\n39 functools.partial(<function check_dict_unchanged at 0x11f155f80>, 'Stree')\n40 functools.partial(<function check_dont_overwrite_parameters at 0x11f158290>, 'Stree')\n41 functools.partial(<function check_fit_idempotent at 0x11f167ef0>, 'Stree')\n42 functools.partial(<function check_n_features_in at 0x11f167f80>, 'Stree')\n43 functools.partial(<function check_requires_y_none at 0x11f16b050>, 'Stree')\n"
     }
    ],
    "source": [
@@ -296,7 +302,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -313,7 +319,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "metadata": {
     "tags": []
    },
@@ -321,7 +327,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "== Not Weighted ===\nSVC train score ..: 0.9820499479708636\nSTree train score : 0.9830905306971904\nSVC test score ...: 0.9842233009708737\nSTree test score .: 0.9848300970873787\n==== Weighted =====\nSVC train score ..: 0.9768470343392299\nSTree train score : 0.9758064516129032\nSVC test score ...: 0.975121359223301\nSTree test score .: 0.9781553398058253\n*SVC test score ..: 0.9433869483836175\n*STree test score : 0.9542572525345916\n"
+     "text": "== Not Weighted ===\nSVC train score ..: 0.9823100936524454\nSTree train score : 0.9830905306971904\nSVC test score ...: 0.9842233009708737\nSTree test score .: 0.9860436893203883\n==== Weighted =====\nSVC train score ..: 0.9799687825182102\nSTree train score : 0.9807492195629552\nSVC test score ...: 0.9848300970873787\nSTree test score .: 0.9830097087378641\n*SVC test score ..: 0.9487167285301864\n*STree test score : 0.9538538933228189\n"
     }
    ],
    "source": [
@@ -347,7 +353,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "metadata": {
     "tags": []
    },
@@ -355,7 +361,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1659\nroot - Down feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.2701\nroot - Down - Down, <cgaf> - Leaf class=1 belief= 0.841270 impurity=0.2671 counts=(array([0, 1]), array([ 60, 318]))\nroot - Down - Up, <pure> - Leaf class=0 belief= 1.000000 impurity=0.0000 counts=(array([0]), array([1]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.990476 impurity=0.0189 counts=(array([0, 1]), array([3432,   33]))\n\n"
+     "text": "root feaures=(0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27) impurity=0.1664\nroot - Down, <cgaf> - Leaf class=1 belief= 0.888268 impurity=0.1985 counts=(array([0, 1]), array([ 40, 318]))\nroot - Up, <cgaf> - Leaf class=0 belief= 0.990247 impurity=0.0193 counts=(array([0, 1]), array([3452,   34]))\n\n"
     }
    ],
    "source": [
@@ -371,7 +377,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "metadata": {
     "tags": []
    },
@@ -379,27 +385,7 @@
     {
      "output_type": "stream",
      "name": "stdout",
-     "text": "****************************************\nmax_features None = 28\nTrain score : 0.9846514047866806\nTest score .: 0.9860436893203883\nTook 5.03 seconds\n****************************************\nmax_features auto = 5\nTrain score : 0.9802289281997919\nTest score .: 0.9824029126213593\nTook 2.20 seconds\n****************************************\nmax_features log2 = 4\nTrain score : 0.9765868886576483\nTest score .: 0.9769417475728155\nTook 0.91 seconds\n****************************************\nmax_features 7 = 7\nTrain score : 0.9768470343392299\nTest score .: 0.9781553398058253\nTook 4.55 seconds\n****************************************\n"
-    },
-    {
-     "output_type": "error",
-     "ename": "KeyboardInterrupt",
-     "evalue": "",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;32m~/.virtualenvs/general/lib/python3.7/site-packages/IPython/core/interactiveshell.py\u001b[0m in \u001b[0;36mrun_code\u001b[0;34m(self, code_obj, result, async_)\u001b[0m\n\u001b[1;32m   3330\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3331\u001b[0;31m                     \u001b[0mexec\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode_obj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_global_ns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0muser_ns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3332\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m<ipython-input-15-ce99359728af>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mStree\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrandom_state\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmax_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mclf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mXtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mytrain\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"max_features {max_features} = {clf.max_features_}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Code/stree/stree/Strees.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    470\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_features_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialize_max_features\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 471\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtree_\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msample_weight\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"root\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    472\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_predictor\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Code/stree/stree/Strees.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, X, y, sample_weight, depth, title)\u001b[0m\n\u001b[1;32m    542\u001b[0m             )\n\u001b[0;32m--> 543\u001b[0;31m         \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_up\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_U\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_u\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msw_u\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdepth\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" - Up\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    544\u001b[0m         \u001b[0mnode\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_down\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_D\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msw_d\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdepth\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtitle\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\" - Down\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Code/stree/stree/Strees.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, X, y, sample_weight, depth, title)\u001b[0m\n\u001b[1;32m    514\u001b[0m         \u001b[0mclf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_clf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 515\u001b[0;31m         \u001b[0mXs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplitter_\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_subspace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax_features_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    516\u001b[0m         \u001b[0;31m# solve WARNING: class label 0 specified in weight is not found\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Code/stree/stree/Strees.py\u001b[0m in \u001b[0;36mget_subspace\u001b[0;34m(self, dataset, labels, max_features)\u001b[0m\n\u001b[1;32m    271\u001b[0m         \"\"\"\n\u001b[0;32m--> 272\u001b[0;31m         \u001b[0mindices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_subspaces_set\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    273\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mdataset\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;32m~/Code/stree/stree/Strees.py\u001b[0m in \u001b[0;36m_get_subspaces_set\u001b[0;34m(self, dataset, labels, max_features)\u001b[0m\n\u001b[1;32m    253\u001b[0m         \u001b[0mfeatures\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 254\u001b[0;31m         \u001b[0mfeatures_sets\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcombinations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_features\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    255\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfeatures_sets\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: ",
-      "\nDuring handling of the above exception, another exception occurred:\n",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
-      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
-     ]
+     "text": "****************************************\nmax_features None = 28\nTrain score : 0.9841311134235172\nTest score .: 0.9848300970873787\nTook 0.35 seconds\n****************************************\nmax_features auto = 5\nTrain score : 0.981009365244537\nTest score .: 0.9830097087378641\nTook 0.71 seconds\n****************************************\nmax_features log2 = 4\nTrain score : 0.9763267429760666\nTest score .: 0.9775485436893204\nTook 0.32 seconds\n****************************************\nmax_features 7 = 7\nTrain score : 0.9750260145681582\nTest score .: 0.9775485436893204\nTook 2.35 seconds\n****************************************\nmax_features 0.5 = 14\nTrain score : 0.9820499479708636\nTest score .: 0.9824029126213593\nTook 84.80 seconds\n****************************************\nmax_features 0.1 = 2\nTrain score : 0.9513527575442248\nTest score .: 0.9526699029126213\nTook 0.25 seconds\n****************************************\nmax_features 0.7 = 19\nTrain score : 0.9841311134235172\nTest score .: 0.9830097087378641\nTook 14.16 seconds\n"
     }
    ],
    "source": [
@@ -413,63 +399,13 @@
     "    print(\"Test score .:\", clf.score(Xtest, ytest))\n",
     "    print(f\"Took {time.time() - now:.2f} seconds\")"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {
-    "tags": [
-     "outputPrepend"
-    ]
-   },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": "(6, 10, 14), (6, 10, 15), (6, 10, 16), (6, 10, 17), (6, 10, 18), (6, 10, 19), (6, 10, 20), (6, 10, 21), (6, 10, 22), (6, 10, 23), (6, 10, 24), (6, 10, 25), (6, 10, 26), (6, 10, 27), (6, 11, 12), (6, 11, 13), (6, 11, 14), (6, 11, 15), (6, 11, 16), (6, 11, 17), (6, 11, 18), (6, 11, 19), (6, 11, 20), (6, 11, 21), (6, 11, 22), (6, 11, 23), (6, 11, 24), (6, 11, 25), (6, 11, 26), (6, 11, 27), (6, 12, 13), (6, 12, 14), (6, 12, 15), (6, 12, 16), (6, 12, 17), (6, 12, 18), (6, 12, 19), (6, 12, 20), (6, 12, 21), (6, 12, 22), (6, 12, 23), (6, 12, 24), (6, 12, 25), (6, 12, 26), (6, 12, 27), (6, 13, 14), (6, 13, 15), (6, 13, 16), (6, 13, 17), (6, 13, 18), (6, 13, 19), (6, 13, 20), (6, 13, 21), (6, 13, 22), (6, 13, 23), (6, 13, 24), (6, 13, 25), (6, 13, 26), (6, 13, 27), (6, 14, 15), (6, 14, 16), (6, 14, 17), (6, 14, 18), (6, 14, 19), (6, 14, 20), (6, 14, 21), (6, 14, 22), (6, 14, 23), (6, 14, 24), (6, 14, 25), (6, 14, 26), (6, 14, 27), (6, 15, 16), (6, 15, 17), (6, 15, 18), (6, 15, 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21, 25), (19, 21, 26), (19, 21, 27), (19, 22, 23), (19, 22, 24), (19, 22, 25), (19, 22, 26), (19, 22, 27), (19, 23, 24), (19, 23, 25), (19, 23, 26), (19, 23, 27), (19, 24, 25), (19, 24, 26), (19, 24, 27), (19, 25, 26), (19, 25, 27), (19, 26, 27), (20, 21, 22), (20, 21, 23), (20, 21, 24), (20, 21, 25), (20, 21, 26), (20, 21, 27), (20, 22, 23), (20, 22, 24), (20, 22, 25), (20, 22, 26), (20, 22, 27), (20, 23, 24), (20, 23, 25), (20, 23, 26), (20, 23, 27), (20, 24, 25), (20, 24, 26), (20, 24, 27), (20, 25, 26), (20, 25, 27), (20, 26, 27), (21, 22, 23), (21, 22, 24), (21, 22, 25), (21, 22, 26), (21, 22, 27), (21, 23, 24), (21, 23, 25), (21, 23, 26), (21, 23, 27), (21, 24, 25), (21, 24, 26), (21, 24, 27), (21, 25, 26), (21, 25, 27), (21, 26, 27), (22, 23, 24), (22, 23, 25), (22, 23, 26), (22, 23, 27), (22, 24, 25), (22, 24, 26), (22, 24, 27), (22, 25, 26), (22, 25, 27), (22, 26, 27), (23, 24, 25), (23, 24, 26), (23, 24, 27), (23, 25, 26), (23, 25, 27), (23, 26, 27), (24, 25, 26), (24, 25, 27), (24, 26, 27), (25, 26, 27)]\n"
-    }
-   ],
-   "source": [
-    "from itertools import combinations\n",
-    "max_features=3\n",
-    "features = range(Xtrain.shape[1])\n",
-    "features_sets = list(combinations(features, max_features))\n",
-    "print(features_sets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {
-    "tags": []
-   },
-   "outputs": [
-    {
-     "output_type": "stream",
-     "name": "stdout",
-     "text": "[(3, 7, 10), (1, 7, 24), (7, 12, 26)]\n"
-    }
-   ],
-   "source": [
-    "import random\n",
-    "if len(features_sets) > 3:\n",
-    "    features_sets = random.sample(features_sets, 3)\n",
-    "print(features_sets)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3.7.6 64-bit ('general': venv)",
    "language": "python",
-   "name": "python37664bitgeneralvenve3128601eb614c5da59c5055670b6040"
+   "name": "python37664bitgeneralvenvfbd0a23e74cf4e778460f5ffc6761f39"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/notebooks/gridsearch.ipynb b/notebooks/gridsearch.ipynb
index 46a180a..fe7402a 100644
--- a/notebooks/gridsearch.ipynb
+++ b/notebooks/gridsearch.ipynb
@@ -66,7 +66,8 @@
         "id": "z9Q-YUfBDZEq",
         "colab_type": "code",
         "colab": {},
-        "outputId": "afc822fb-f16a-4302-8a67-2b9e2880159b"
+        "outputId": "afc822fb-f16a-4302-8a67-2b9e2880159b",
+        "tags": []
       },
       "source": [
         "random_state=1\n",
@@ -112,7 +113,7 @@
         {
           "output_type": "stream",
           "name": "stdout",
-          "text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28)  y.shape (1492,)\nFraud: 33.244% 496\nValid: 66.756% 996\n"
+          "text": "Fraud: 0.173% 492\nValid: 99.827% 284315\nX.shape (1492, 28)  y.shape (1492,)\nFraud: 32.976% 492\nValid: 67.024% 1000\n"
         }
       ]
     },
@@ -137,25 +138,25 @@
         "    'learning_rate': [.5, 1],\n",
         "    'base_estimator__tol': [.1,  1e-02],\n",
         "    'base_estimator__max_depth': [3, 5],\n",
-        "    'base_estimator__C': [1, 3],\n",
+        "    'base_estimator__C': [7, 55],\n",
         "    'base_estimator__kernel': ['linear', 'poly', 'rbf']\n",
         "}"
       ],
-      "execution_count": 9,
+      "execution_count": 5,
       "outputs": []
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": 6,
       "metadata": {},
       "outputs": [
         {
           "output_type": "execute_result",
           "data": {
-            "text/plain": "{'C': 1.0,\n 'degree': 3,\n 'gamma': 'scale',\n 'kernel': 'linear',\n 'max_depth': None,\n 'max_iter': 1000,\n 'min_samples_split': 0,\n 'random_state': None,\n 'tol': 0.0001}"
+            "text/plain": "{'C': 1.0,\n 'criterion': 'gini',\n 'degree': 3,\n 'gamma': 'scale',\n 'kernel': 'linear',\n 'max_depth': None,\n 'max_features': None,\n 'max_iter': 1000,\n 'min_samples_split': 0,\n 'random_state': None,\n 'split_criteria': 'max_samples',\n 'splitter': 'random',\n 'tol': 0.0001}"
           },
           "metadata": {},
-          "execution_count": 14
+          "execution_count": 6
         }
       ],
       "source": [
@@ -168,28 +169,29 @@
         "id": "CrcB8o6EDZE5",
         "colab_type": "code",
         "colab": {},
-        "outputId": "7703413a-d563-4289-a13b-532f38f82762"
+        "outputId": "7703413a-d563-4289-a13b-532f38f82762",
+        "tags": []
       },
       "source": [
         "random_state=2020\n",
-        "clf = AdaBoostClassifier(random_state=random_state)\n",
+        "clf = AdaBoostClassifier(random_state=random_state, algorithm=\"SAMME\")\n",
         "grid = GridSearchCV(clf, parameters, verbose=10, n_jobs=-1, return_train_score=True)\n",
         "grid.fit(Xtrain, ytrain)"
       ],
-      "execution_count": 11,
+      "execution_count": 7,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
-          "text": "Fitting 5 folds for each of 96 candidates, totalling 480 fits\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    3.6s\n[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    4.2s\n[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    4.8s\n[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    5.3s\n[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    6.2s\n[Parallel(n_jobs=-1)]: Done  45 tasks      | elapsed:    7.2s\n[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    8.9s\n[Parallel(n_jobs=-1)]: Done  69 tasks      | elapsed:   10.7s\n[Parallel(n_jobs=-1)]: Done  82 tasks      | elapsed:   12.7s\n[Parallel(n_jobs=-1)]: Done  97 tasks      | elapsed:   16.7s\n[Parallel(n_jobs=-1)]: Done 112 tasks      | elapsed:   19.4s\n[Parallel(n_jobs=-1)]: Done 129 tasks      | elapsed:   24.4s\n[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   29.3s\n[Parallel(n_jobs=-1)]: Done 165 tasks      | elapsed:   32.7s\n[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   36.4s\n[Parallel(n_jobs=-1)]: Done 205 tasks      | elapsed:   39.7s\n[Parallel(n_jobs=-1)]: Done 226 tasks      | elapsed:   43.7s\n[Parallel(n_jobs=-1)]: Done 249 tasks      | elapsed:   46.6s\n[Parallel(n_jobs=-1)]: Done 272 tasks      | elapsed:   48.8s\n[Parallel(n_jobs=-1)]: Done 297 tasks      | elapsed:   52.0s\n[Parallel(n_jobs=-1)]: Done 322 tasks      | elapsed:   55.9s\n[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:  1.0min\n[Parallel(n_jobs=-1)]: Done 376 tasks      | elapsed:  1.2min\n[Parallel(n_jobs=-1)]: Done 405 tasks      | elapsed:  1.3min\n[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:  1.3min\n[Parallel(n_jobs=-1)]: Done 465 tasks      | elapsed:  1.4min\n[Parallel(n_jobs=-1)]: Done 480 out of 480 | elapsed:  1.5min finished\n"
+          "text": "Fitting 5 folds for each of 96 candidates, totalling 480 fits\n[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n[Parallel(n_jobs=-1)]: Done   2 tasks      | elapsed:    2.0s\n[Parallel(n_jobs=-1)]: Done   9 tasks      | elapsed:    2.4s\n[Parallel(n_jobs=-1)]: Done  16 tasks      | elapsed:    2.7s\n[Parallel(n_jobs=-1)]: Done  25 tasks      | elapsed:    3.3s\n[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    4.3s\n[Parallel(n_jobs=-1)]: Done  45 tasks      | elapsed:    5.3s\n[Parallel(n_jobs=-1)]: Done  56 tasks      | elapsed:    6.6s\n[Parallel(n_jobs=-1)]: Done  69 tasks      | elapsed:    8.1s\n[Parallel(n_jobs=-1)]: Done  82 tasks      | elapsed:    9.4s\n[Parallel(n_jobs=-1)]: Done  97 tasks      | elapsed:   10.1s\n[Parallel(n_jobs=-1)]: Done 112 tasks      | elapsed:   11.1s\n[Parallel(n_jobs=-1)]: Done 129 tasks      | elapsed:   12.3s\n[Parallel(n_jobs=-1)]: Done 146 tasks      | elapsed:   13.6s\n[Parallel(n_jobs=-1)]: Done 165 tasks      | elapsed:   14.9s\n[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   16.2s\n[Parallel(n_jobs=-1)]: Done 205 tasks      | elapsed:   17.6s\n[Parallel(n_jobs=-1)]: Done 226 tasks      | elapsed:   19.1s\n[Parallel(n_jobs=-1)]: Done 249 tasks      | elapsed:   21.6s\n[Parallel(n_jobs=-1)]: Done 272 tasks      | elapsed:   25.9s\n[Parallel(n_jobs=-1)]: Done 297 tasks      | elapsed:   30.4s\n[Parallel(n_jobs=-1)]: Done 322 tasks      | elapsed:   36.7s\n[Parallel(n_jobs=-1)]: Done 349 tasks      | elapsed:   38.1s\n[Parallel(n_jobs=-1)]: Done 376 tasks      | elapsed:   39.6s\n[Parallel(n_jobs=-1)]: Done 405 tasks      | elapsed:   41.9s\n[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:   44.9s\n[Parallel(n_jobs=-1)]: Done 465 tasks      | elapsed:   48.2s\n[Parallel(n_jobs=-1)]: Done 480 out of 480 | elapsed:   49.2s finished\n"
         },
         {
           "output_type": "execute_result",
           "data": {
-            "text/plain": "GridSearchCV(estimator=AdaBoostClassifier(random_state=2020), n_jobs=-1,\n             param_grid={'base_estimator': [Stree(C=1, max_depth=3, tol=0.1)],\n                         'base_estimator__C': [1, 3],\n                         'base_estimator__kernel': ['linear', 'poly', 'rbf'],\n                         'base_estimator__max_depth': [3, 5],\n                         'base_estimator__tol': [0.1, 0.01],\n                         'learning_rate': [0.5, 1], 'n_estimators': [10, 25]},\n             return_train_score=True, verbose=10)"
+            "text/plain": "GridSearchCV(estimator=AdaBoostClassifier(algorithm='SAMME', random_state=2020),\n             n_jobs=-1,\n             param_grid={'base_estimator': [Stree(C=55, max_depth=3, tol=0.01)],\n                         'base_estimator__C': [7, 55],\n                         'base_estimator__kernel': ['linear', 'poly', 'rbf'],\n                         'base_estimator__max_depth': [3, 5],\n                         'base_estimator__tol': [0.1, 0.01],\n                         'learning_rate': [0.5, 1], 'n_estimators': [10, 25]},\n             return_train_score=True, verbose=10)"
           },
           "metadata": {},
-          "execution_count": 11
+          "execution_count": 7
         }
       ]
     },
@@ -199,19 +201,20 @@
         "id": "ZjX88NoYDZE8",
         "colab_type": "code",
         "colab": {},
-        "outputId": "285163c8-fa33-4915-8ae7-61c4f7844344"
+        "outputId": "285163c8-fa33-4915-8ae7-61c4f7844344",
+        "tags": []
       },
       "source": [
         "print(\"Best estimator: \", grid.best_estimator_)\n",
         "print(\"Best hyperparameters: \", grid.best_params_)\n",
         "print(\"Best accuracy: \", grid.best_score_)"
       ],
-      "execution_count": 16,
+      "execution_count": 8,
       "outputs": [
         {
           "output_type": "stream",
           "name": "stdout",
-          "text": "Best estimator:  AdaBoostClassifier(base_estimator=Stree(C=1, max_depth=3, tol=0.1),\n                   learning_rate=0.5, n_estimators=10, random_state=2020)\nBest hyperparameters:  {'base_estimator': Stree(C=1, max_depth=3, tol=0.1), 'base_estimator__C': 1, 'base_estimator__kernel': 'linear', 'base_estimator__max_depth': 3, 'base_estimator__tol': 0.1, 'learning_rate': 0.5, 'n_estimators': 10}\nBest accuracy:  0.9492316893632683\n"
+          "text": "Best estimator:  AdaBoostClassifier(algorithm='SAMME',\n                   base_estimator=Stree(C=55, max_depth=3, tol=0.01),\n                   learning_rate=0.5, n_estimators=25, random_state=2020)\nBest hyperparameters:  {'base_estimator': Stree(C=55, max_depth=3, tol=0.01), 'base_estimator__C': 55, 'base_estimator__kernel': 'linear', 'base_estimator__max_depth': 3, 'base_estimator__tol': 0.01, 'learning_rate': 0.5, 'n_estimators': 25}\nBest accuracy:  0.9559440559440558\n"
         }
       ]
     }
diff --git a/setup.py b/setup.py
index b31557f..a75e418 100644
--- a/setup.py
+++ b/setup.py
@@ -1,6 +1,6 @@
 import setuptools
 
-__version__ = "0.9rc4"
+__version__ = "0.9rc5"
 __author__ = "Ricardo Montañana Gómez"