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@@ -1,9 +1,12 @@
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# STree
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[](https://app.codeship.com/projects/399170)
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[](https://codecov.io/gh/doctorado-ml/stree)
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[](https://www.codacy.com/gh/Doctorado-ML/STree?utm_source=github.com&utm_medium=referral&utm_content=Doctorado-ML/STree&utm_campaign=Badge_Grade)
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[](https://lgtm.com/projects/g/Doctorado-ML/STree/context:python)
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[](https://badge.fury.io/py/STree)
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[](https://zenodo.org/badge/latestdoi/262658230)
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Oblique Tree classifier based on SVM nodes. The nodes are built and splitted with sklearn SVC models. Stree is a sklearn estimator and can be integrated in pipelines, grid searches, etc.
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@@ -16,8 +16,28 @@ from mufs import MUFS
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class Snode:
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"""Nodes of the tree that keeps the svm classifier and if testing the
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"""
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Nodes of the tree that keeps the svm classifier and if testing the
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dataset assigned to it
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Parameters
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----------
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clf : SVC
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Classifier used
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X : np.ndarray
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input dataset in train time (only in testing)
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y : np.ndarray
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input labes in train time
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features : np.array
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features used to compute hyperplane
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impurity : float
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impurity of the node
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title : str
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label describing the route to the node
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weight : np.ndarray, optional
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weights applied to input dataset in train time, by default None
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scaler : StandardScaler, optional
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scaler used if any, by default None
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"""
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def __init__(
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@@ -165,6 +185,55 @@ class Siterator:
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class Splitter:
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"""
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Splits a dataset in two based on different criteria
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Parameters
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----------
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clf : SVC, optional
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classifier, by default None
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criterion : str, optional
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The function to measure the quality of a split (only used if
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max_features != num_features). Supported criteria are “gini” for the
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Gini impurity and “entropy” for the information gain., by default
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"entropy", by default None
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feature_select : str, optional
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The strategy used to choose the feature set at each node (only used if
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max_features < num_features). Supported strategies are: “best”: sklearn
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SelectKBest algorithm is used in every node to choose the max_features
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best features. “random”: The algorithm generates 5 candidates and
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choose the best (max. info. gain) of them. "mutual": Chooses the best
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features w.r.t. their mutual info with the label. "cfs": Apply
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Correlation-based Feature Selection. "fcbf": Apply Fast Correlation-
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Based, by default None
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criteria : str, optional
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ecides (just in case of a multi class classification) which column
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(class) use to split the dataset in a node. max_samples is
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incompatible with 'ovo' multiclass_strategy, by default None
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min_samples_split : int, optional
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The minimum number of samples required to split an internal node. 0
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(default) for any, by default None
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random_state : optional
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Controls the pseudo random number generation for shuffling the data for
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probability estimates. Ignored when probability is False.Pass an int
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for reproducible output across multiple function calls, by
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default None
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normalize : bool, optional
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If standardization of features should be applied on each node with the
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samples that reach it , by default False
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Raises
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------
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ValueError
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clf has to be a sklearn estimator
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ValueError
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criterion must be gini or entropy
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ValueError
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criteria has to be max_samples or impurity
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ValueError
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splitter must be in {random, best, mutual, cfs, fcbf}
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"""
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def __init__(
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self,
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clf: SVC = None,
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@@ -175,6 +244,7 @@ class Splitter:
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random_state=None,
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normalize=False,
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):
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self._clf = clf
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self._random_state = random_state
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if random_state is not None:
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109
stree/Strees.py
109
stree/Strees.py
@@ -20,11 +20,117 @@ from .Splitter import Splitter, Snode, Siterator
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class Stree(BaseEstimator, ClassifierMixin):
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"""Estimator that is based on binary trees of svm nodes
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"""
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Estimator that is based on binary trees of svm nodes
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can deal with sample_weights in predict, used in boosting sklearn methods
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inheriting from BaseEstimator implements get_params and set_params methods
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inheriting from ClassifierMixin implement the attribute _estimator_type
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with "classifier" as value
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Parameters
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----------
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C : float, optional
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Regularization parameter. The strength of the regularization is
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inversely proportional to C. Must be strictly positive., by default 1.0
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kernel : str, optional
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Specifies the kernel type to be used in the algorithm. It must be one
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of ‘liblinear’, ‘linear’, ‘poly’ or ‘rbf’. liblinear uses
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[liblinear](https://www.csie.ntu.edu.tw/~cjlin/liblinear/) library and
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the rest uses [libsvm](https://www.csie.ntu.edu.tw/~cjlin/libsvm/)
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library through scikit-learn library, by default "linear"
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max_iter : int, optional
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Hard limit on iterations within solver, or -1 for no limit., by default
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1e5
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random_state : int, optional
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Controls the pseudo random number generation for shuffling the data for
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probability estimates. Ignored when probability is False.Pass an int
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for reproducible output across multiple function calls, by
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default None
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max_depth : int, optional
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Specifies the maximum depth of the tree, by default None
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tol : float, optional
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Tolerance for stopping, by default 1e-4
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degree : int, optional
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Degree of the polynomial kernel function (‘poly’). Ignored by all other
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kernels., by default 3
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gamma : str, optional
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Kernel coefficient for ‘rbf’, ‘poly’ and ‘sigmoid’.if gamma='scale'
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(default) is passed then it uses 1 / (n_features * X.var()) as value
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of gamma,if ‘auto’, uses 1 / n_features., by default "scale"
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split_criteria : str, optional
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Decides (just in case of a multi class classification) which column
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(class) use to split the dataset in a node. max_samples is
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incompatible with 'ovo' multiclass_strategy, by default "impurity"
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criterion : str, optional
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The function to measure the quality of a split (only used if
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max_features != num_features). Supported criteria are “gini” for the
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Gini impurity and “entropy” for the information gain., by default
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"entropy"
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min_samples_split : int, optional
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The minimum number of samples required to split an internal node. 0
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(default) for any, by default 0
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max_features : optional
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The number of features to consider when looking for the split: If int,
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then consider max_features features at each split. If float, then
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max_features is a fraction and int(max_features * n_features) features
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are considered at each split. If “auto”, then max_features=
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sqrt(n_features). If “sqrt”, then max_features=sqrt(n_features). If
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“log2”, then max_features=log2(n_features). If None, then max_features=
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n_features., by default None
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splitter : str, optional
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The strategy used to choose the feature set at each node (only used if
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max_features < num_features). Supported strategies are: “best”: sklearn
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SelectKBest algorithm is used in every node to choose the max_features
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best features. “random”: The algorithm generates 5 candidates and
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choose the best (max. info. gain) of them. "mutual": Chooses the best
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features w.r.t. their mutual info with the label. "cfs": Apply
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Correlation-based Feature Selection. "fcbf": Apply Fast Correlation-
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Based , by default "random"
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multiclass_strategy : str, optional
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Strategy to use with multiclass datasets, "ovo": one versus one. "ovr":
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one versus rest, by default "ovo"
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normalize : bool, optional
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If standardization of features should be applied on each node with the
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samples that reach it , by default False
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Attributes
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----------
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classes_ : ndarray of shape (n_classes,)
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The classes labels.
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n_classes_ : int
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The number of classes
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n_iter_ : int
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Max number of iterations in classifier
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depth_ : int
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Max depht of the tree
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n_features_ : int
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The number of features when ``fit`` is performed.
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n_features_in_ : int
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Number of features seen during :term:`fit`.
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max_features_ : int
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Number of features to use in hyperplane computation
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tree_ : Node
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root of the tree
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X_ : ndarray
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points to the input dataset
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y_ : ndarray
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points to the input labels
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References
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----------
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R. Montañana, J. A. Gámez, J. M. Puerta, "STree: a single multi-class
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oblique decision tree based on support vector machines.", 2021 LNAI...
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"""
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def __init__(
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@@ -45,6 +151,7 @@ class Stree(BaseEstimator, ClassifierMixin):
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multiclass_strategy: str = "ovo",
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normalize: bool = False,
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):
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self.max_iter = max_iter
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self.C = C
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self.kernel = kernel
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