mirror of
https://github.com/Doctorado-ML/benchmark.git
synced 2025-08-17 16:35:54 +00:00
fix: 🧪 Fix tests with new scikit-learn version
This commit is contained in:
@@ -46,7 +46,7 @@ def main(args_test=None):
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'{"C": 7, "gamma": 0.1, "kernel": "rbf", "multiclass_strategy": '
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'{"C": 7, "gamma": 0.1, "kernel": "rbf", "multiclass_strategy": '
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'"ovr"}',
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'"ovr"}',
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'{"C": 5, "kernel": "rbf", "gamma": "auto"}',
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'{"C": 5, "kernel": "rbf", "gamma": "auto"}',
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'{"C": 0.05, "max_iter": 10000.0, "kernel": "liblinear", '
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'{"C": 0.05, "max_iter": 10000, "kernel": "liblinear", '
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'"multiclass_strategy": "ovr"}',
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'"multiclass_strategy": "ovr"}',
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'{"C":0.0275, "kernel": "liblinear", "multiclass_strategy": "ovr"}',
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'{"C":0.0275, "kernel": "liblinear", "multiclass_strategy": "ovr"}',
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'{"C": 7, "gamma": 10.0, "kernel": "rbf", "multiclass_strategy": '
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'{"C": 7, "gamma": 10.0, "kernel": "rbf", "multiclass_strategy": '
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@@ -97,7 +97,7 @@ def main(args_test=None):
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for item in results:
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for item in results:
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results_tmp = {"n_jobs": [-1], "n_estimators": [100]}
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results_tmp = {"n_jobs": [-1], "n_estimators": [100]}
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for key, value in results[item].items():
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for key, value in results[item].items():
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new_key = f"base_estimator__{key}"
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new_key = f"estimator__{key}"
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try:
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try:
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results_tmp[new_key] = sorted(value)
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results_tmp[new_key] = sorted(value)
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except TypeError:
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except TypeError:
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@@ -111,6 +111,7 @@ def main(args_test=None):
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t2 = sorted([x for x in value if isinstance(x, str)])
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t2 = sorted([x for x in value if isinstance(x, str)])
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results_tmp[new_key] = t1 + t2
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results_tmp[new_key] = t1 + t2
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output.append(results_tmp)
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output.append(results_tmp)
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# save results
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# save results
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file_name = Files.grid_input(args.score, args.model)
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file_name = Files.grid_input(args.score, args.model)
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file_output = os.path.join(Folders.results, file_name)
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file_output = os.path.join(Folders.results, file_name)
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@@ -18,7 +18,7 @@ class BestResultTest(TestBase):
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"C": 7,
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"C": 7,
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"gamma": 0.1,
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"gamma": 0.1,
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"kernel": "rbf",
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"kernel": "rbf",
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"max_iter": 10000.0,
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"max_iter": 10000,
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"multiclass_strategy": "ovr",
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"multiclass_strategy": "ovr",
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},
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},
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"results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json",
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"results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json",
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@@ -52,7 +52,7 @@ class ExperimentTest(TestBase):
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"C": 7,
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"C": 7,
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"gamma": 0.1,
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"gamma": 0.1,
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"kernel": "rbf",
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"kernel": "rbf",
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"max_iter": 10000.0,
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"max_iter": 10000,
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"multiclass_strategy": "ovr",
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"multiclass_strategy": "ovr",
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},
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},
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"results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json",
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"results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json",
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@@ -1 +1 @@
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{"balance-scale": [0.98, {"splitter": "best", "max_features": "auto"}, "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json"], "balloons": [0.86, {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000.0, "multiclass_strategy": "ovr"}, "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"]}
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{"balance-scale": [0.98, {"splitter": "best", "max_features": "auto"}, "results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json"], "balloons": [0.86, {"C": 7, "gamma": 0.1, "kernel": "rbf", "max_iter": 10000, "multiclass_strategy": "ovr"}, "results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json"]}
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@@ -17,10 +17,10 @@
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"features": 4,
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"features": 4,
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"classes": 3,
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"classes": 3,
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"hyperparameters": {
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"hyperparameters": {
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"C": 10000.0,
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"C": 10000,
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"gamma": 0.1,
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"gamma": 0.1,
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"kernel": "rbf",
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"kernel": "rbf",
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"max_iter": 10000.0,
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"max_iter": 10000,
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"multiclass_strategy": "ovr"
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"multiclass_strategy": "ovr"
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},
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},
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"nodes": 7.0,
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"nodes": 7.0,
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@@ -40,7 +40,7 @@
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"C": 7,
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"C": 7,
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"gamma": 0.1,
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"gamma": 0.1,
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"kernel": "rbf",
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"kernel": "rbf",
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"max_iter": 10000.0,
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"max_iter": 10000,
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"multiclass_strategy": "ovr"
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"multiclass_strategy": "ovr"
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},
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},
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"nodes": 3.0,
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"nodes": 3.0,
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@@ -6,13 +6,13 @@
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"n_estimators": [
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"n_estimators": [
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100
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100
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],
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],
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"base_estimator__C": [
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"estimator__C": [
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1.0
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1.0
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],
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],
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"base_estimator__kernel": [
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"estimator__kernel": [
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"linear"
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"linear"
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],
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],
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"base_estimator__multiclass_strategy": [
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"estimator__multiclass_strategy": [
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"ovo"
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"ovo"
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]
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]
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},
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},
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@@ -23,7 +23,7 @@
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"n_estimators": [
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"n_estimators": [
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100
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100
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],
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],
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"base_estimator__C": [
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"estimator__C": [
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0.001,
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0.001,
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0.0275,
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0.0275,
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0.05,
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0.05,
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@@ -36,10 +36,10 @@
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7,
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7,
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10000.0
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10000.0
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],
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],
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"base_estimator__kernel": [
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"estimator__kernel": [
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"liblinear"
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"liblinear"
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],
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],
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"base_estimator__multiclass_strategy": [
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"estimator__multiclass_strategy": [
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"ovr"
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"ovr"
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]
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]
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},
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},
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@@ -50,7 +50,7 @@
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"n_estimators": [
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"n_estimators": [
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100
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100
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],
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],
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"base_estimator__C": [
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"estimator__C": [
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0.05,
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0.05,
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1.0,
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1.0,
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1.05,
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1.05,
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@@ -62,7 +62,7 @@
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57,
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57,
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10000.0
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10000.0
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],
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],
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"base_estimator__gamma": [
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"estimator__gamma": [
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0.001,
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0.001,
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0.1,
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0.1,
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0.14,
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0.14,
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@@ -70,10 +70,10 @@
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"auto",
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"auto",
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"scale"
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"scale"
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],
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],
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"base_estimator__kernel": [
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"estimator__kernel": [
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"rbf"
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"rbf"
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],
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],
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"base_estimator__multiclass_strategy": [
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"estimator__multiclass_strategy": [
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"ovr"
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"ovr"
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]
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]
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},
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},
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@@ -84,20 +84,20 @@
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"n_estimators": [
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"n_estimators": [
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100
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100
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],
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],
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"base_estimator__C": [
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"estimator__C": [
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0.05,
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0.05,
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0.2,
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0.2,
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1.0,
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1.0,
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8.25
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8.25
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],
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],
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"base_estimator__gamma": [
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"estimator__gamma": [
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0.1,
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0.1,
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"scale"
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"scale"
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],
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],
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"base_estimator__kernel": [
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"estimator__kernel": [
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"poly"
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"poly"
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],
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],
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"base_estimator__multiclass_strategy": [
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"estimator__multiclass_strategy": [
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"ovo",
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"ovo",
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"ovr"
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"ovr"
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]
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]
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@@ -9,7 +9,7 @@
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Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters
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Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters
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============================== ====== ===== === ======= ======= ======= =============== ================= ===============
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============================== ====== ===== === ======= ======= ======= =============== ================= ===============
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[96mbalance-scale 625 4 3 23.32 12.16 6.44 0.840160±0.0304 0.013745±0.0019 {'splitter': 'best', 'max_features': 'auto'}
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[96mbalance-scale 625 4 3 23.32 12.16 6.44 0.840160±0.0304 0.013745±0.0019 {'splitter': 'best', 'max_features': 'auto'}
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[94mballoons 16 4 2 3.00 2.00 2.00 0.860000±0.2850 0.000388±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}
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[94mballoons 16 4 2 3.00 2.00 2.00 0.860000±0.2850 0.000388±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
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[94m*************************************************************************************************************************
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[94m*************************************************************************************************************************
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[94m* accuracy compared to STree_default (liblinear-ovr) .: 0.0422 *
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[94m* accuracy compared to STree_default (liblinear-ovr) .: 0.0422 *
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[94m*************************************************************************************************************************
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[94m*************************************************************************************************************************
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@@ -32,7 +32,7 @@
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7;9;"0.0150468069702512"
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7;9;"0.0150468069702512"
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7;10;"0.01404867172241211"
|
7;10;"0.01404867172241211"
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7;11;"0.002026269126958884"
|
7;11;"0.002026269126958884"
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7;12;"{'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
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7;12;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
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8;1;"balloons"
|
8;1;"balloons"
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8;2;"16"
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8;2;"16"
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8;3;"4"
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8;3;"4"
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@@ -44,5 +44,5 @@
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8;9;"0.2850146195080759"
|
8;9;"0.2850146195080759"
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8;10;"0.0008541679382324218"
|
8;10;"0.0008541679382324218"
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8;11;"3.629469326417878e-05"
|
8;11;"3.629469326417878e-05"
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8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
|
8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
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10;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0454"
|
10;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0454"
|
@@ -32,7 +32,7 @@
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7;10;0.0150468069702512
|
7;10;0.0150468069702512
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7;11;0.01404867172241211
|
7;11;0.01404867172241211
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7;12;0.002026269126958884
|
7;12;0.002026269126958884
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7;13;"{'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
|
7;13;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
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8;1;"balloons"
|
8;1;"balloons"
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8;2;16
|
8;2;16
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8;3;4
|
8;3;4
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@@ -45,7 +45,7 @@
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8;10;0.2850146195080759
|
8;10;0.2850146195080759
|
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8;11;0.0008541679382324218
|
8;11;0.0008541679382324218
|
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8;12;3.629469326417878e-05
|
8;12;3.629469326417878e-05
|
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8;13;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
|
8;13;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
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11;2;"✔"
|
11;2;"✔"
|
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11;3;1
|
11;3;1
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11;4;"Equal to best"
|
11;4;"Equal to best"
|
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|
@@ -32,7 +32,7 @@
|
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7;9;"0.0150468069702512"
|
7;9;"0.0150468069702512"
|
||||||
7;10;"0.01404867172241211"
|
7;10;"0.01404867172241211"
|
||||||
7;11;"0.002026269126958884"
|
7;11;"0.002026269126958884"
|
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7;12;"{'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
|
7;12;"{'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
|
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8;1;"balloons"
|
8;1;"balloons"
|
||||||
8;2;"16"
|
8;2;"16"
|
||||||
8;3;"4"
|
8;3;"4"
|
||||||
@@ -44,5 +44,5 @@
|
|||||||
8;9;"0.2850146195080759"
|
8;9;"0.2850146195080759"
|
||||||
8;10;"0.0008541679382324218"
|
8;10;"0.0008541679382324218"
|
||||||
8;11;"3.629469326417878e-05"
|
8;11;"3.629469326417878e-05"
|
||||||
8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}"
|
8;12;"{'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}"
|
||||||
10;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0454"
|
10;1;"** accuracy compared to STree_default (liblinear-ovr) .: 0.0454"
|
||||||
|
@@ -8,8 +8,8 @@
|
|||||||
|
|
||||||
Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters
|
Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters
|
||||||
============================== ====== ===== === ======= ======= ======= =============== ================= ===============
|
============================== ====== ===== === ======= ======= ======= =============== ================= ===============
|
||||||
[96mbalance-scale 625 4 3 7.00 4.00 3.00 0.970560±0.0150 0.014049±0.0020 {'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}
|
[96mbalance-scale 625 4 3 7.00 4.00 3.00 0.970560±0.0150 0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
|
||||||
[94mballoons 16 4 2 3.00 2.00 2.00 0.860000±0.2850 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}
|
[94mballoons 16 4 2 3.00 2.00 2.00 0.860000±0.2850 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
|
||||||
[94m*************************************************************************************************************************
|
[94m*************************************************************************************************************************
|
||||||
[94m* accuracy compared to STree_default (liblinear-ovr) .: 0.0454 *
|
[94m* accuracy compared to STree_default (liblinear-ovr) .: 0.0454 *
|
||||||
[94m*************************************************************************************************************************
|
[94m*************************************************************************************************************************
|
||||||
|
@@ -5,7 +5,7 @@
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|||||||
Dataset Score File/Message Hyperparameters
|
Dataset Score File/Message Hyperparameters
|
||||||
============================== ======== ============================================================================ =============================================
|
============================== ======== ============================================================================ =============================================
|
||||||
balance-scale 0.980000 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json {'splitter': 'best', 'max_features': 'auto'}
|
balance-scale 0.980000 results_accuracy_STree_iMac27_2021-10-27_09:40:40_0.json {'splitter': 'best', 'max_features': 'auto'}
|
||||||
balloons 0.860000 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}
|
balloons 0.860000 results_accuracy_STree_iMac27_2021-09-30_11:42:07_0.json {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
|
||||||
******************************************************************************************************************************************************************
|
******************************************************************************************************************************************************************
|
||||||
* accuracy compared to STree_default (liblinear-ovr) .: 0.0457 *
|
* accuracy compared to STree_default (liblinear-ovr) .: 0.0457 *
|
||||||
******************************************************************************************************************************************************************
|
******************************************************************************************************************************************************************
|
||||||
|
@@ -8,8 +8,8 @@
|
|||||||
|
|
||||||
Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters
|
Dataset Sampl. Feat. Cls Nodes Leaves Depth Score Time Hyperparameters
|
||||||
============================== ====== ===== === ======= ======= ======= =============== ================= ===============
|
============================== ====== ===== === ======= ======= ======= =============== ================= ===============
|
||||||
[96mbalance-scale 625 4 3 7.00 4.00 3.00 0.970560±0.0150 0.014049±0.0020 {'C': 10000.0, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}
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[96mbalance-scale 625 4 3 7.00 4.00 3.00 0.970560±0.0150 0.014049±0.0020 {'C': 10000, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
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[94mballoons 16 4 2 3.00 2.00 2.00 0.860000±0.2850✔ 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000.0, 'multiclass_strategy': 'ovr'}
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[94mballoons 16 4 2 3.00 2.00 2.00 0.860000±0.2850✔ 0.000854±0.0000 {'C': 7, 'gamma': 0.1, 'kernel': 'rbf', 'max_iter': 10000, 'multiclass_strategy': 'ovr'}
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[94m*************************************************************************************************************************
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[94m*************************************************************************************************************************
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[94m* ✔ Equal to best .....: 1 *
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[94m* ✔ Equal to best .....: 1 *
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[94m* accuracy compared to STree_default (liblinear-ovr) .: 0.0454 *
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[94m* accuracy compared to STree_default (liblinear-ovr) .: 0.0454 *
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Reference in New Issue
Block a user