sklearn.tree.DecisionTreeClassifier: Difference between revisions
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* <B>Context</B> | * <B>Context</B> | ||
** Usage: | ** Usage: | ||
::: 1) Import [[Classification Tree Learning System]] from [[scikit-learn]] : <code>from [[sklearn.tree]] import [[DecisionTreeClassifier]]</code> | ::: 1) Import [[Classification Tree Learning System]] from [[scikit-learn]] : <code>from [[sklearn.tree]] import [[sklearn.tree.DecisionTreeClassifier|DecisionTreeClassifier]]</code> | ||
::: 2) Create [[design matrix]] <code>X</code> and [[response vector]] <code>Y</code> | ::: 2) Create [[design matrix]] <code>X</code> and [[response vector]] <code>Y</code> | ||
::: 3) Create [[Decision Tree Classifier]] object: <code>DTclf=[[DecisionTreeClassifier]](criterion=’gini’, splitter=’best’[, max_depth=None, min_samples_split=2, min_samples_leaf=1,...])</code> | ::: 3) Create [[Decision Tree Classifier]] object: <code>DTclf=[[sklearn.tree.DecisionTreeClassifier|DecisionTreeClassifier]](criterion=’gini’, splitter=’best’[, max_depth=None, min_samples_split=2, min_samples_leaf=1,...])</code> | ||
::: 4) Choose method(s): | ::: 4) Choose method(s): | ||
::::* <code>DTclf</code>.<code>apply(X[, check_input])</code>, returns the leaf index for each sample predictor. | ::::* <code>DTclf</code>.<code>apply(X[, check_input])</code>, returns the leaf index for each sample predictor. | ||
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=== 2017b === | === 2017b === | ||
* (Scikit-Learn, 2017B) ⇒ http://scikit-learn.org/stable/modules/tree.html#classification | * (Scikit-Learn, 2017B) ⇒ http://scikit-learn.org/stable/modules/tree.html#classification | ||
** QUOTE: [[DecisionTreeClassifier]] is a [[class]] capable of performing [[multi-class classification]] on a [[dataset]]. <P> As with other [[classifier]]s, [[DecisionTreeClassifier]] takes as input two arrays: an array X, sparse or dense, of size <code>[n_samples, n_features]</code> holding the [[training sample]]s, and an array Y of integer values, size <code>[n_samples]</code>, holding the class labels for the training samples: | ** QUOTE: [[sklearn.tree.DecisionTreeClassifier|DecisionTreeClassifier]] is a [[class]] capable of performing [[multi-class classification]] on a [[dataset]]. <P> As with other [[classifier]]s, [[sklearn.tree.DecisionTreeClassifier|DecisionTreeClassifier]] takes as input two arrays: an array X, sparse or dense, of size <code>[n_samples, n_features]</code> holding the [[training sample]]s, and an array Y of integer values, size <code>[n_samples]</code>, holding the class labels for the training samples: | ||
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__NOTOC__ | __NOTOC__ | ||
[[Category:Concept]] | [[Category:Concept]] |
Revision as of 20:45, 23 December 2019
A sklearn.tree.DecisionTreeClassifier is a classification tree learning system within sklearn.tree
.
- Context
- Usage:
- 1) Import Classification Tree Learning System from scikit-learn :
from sklearn.tree import DecisionTreeClassifier
- 2) Create design matrix
X
and response vectorY
- 3) Create Decision Tree Classifier object:
DTclf=DecisionTreeClassifier(criterion=’gini’, splitter=’best’[, max_depth=None, min_samples_split=2, min_samples_leaf=1,...])
- 4) Choose method(s):
DTclf
.apply(X[, check_input])
, returns the leaf index for each sample predictor.DTclf
.decision_path(X[, check_input])
, returns the decision path in the tree.DTclf
.fit(X, y[, sample_weight, check_input,...])
builds a decision tree classifier from the training set (X, y).DTclf
.get_params([deep])
returns parameters for this estimator.DTclf
.predict(X[, check_input])
, predicts class for X.DTclf
.predict_log_proba(X)
, predicts class log-probabilities of the input samples X.DTclf
.predict_proba(X[, check_input])
, predicts class probabilities of the input samples X.DTclf
.score(X, y[, sample_weight])
, returns the mean accuracy on the given test data and labels.DTclf
.set_params(**params)
, sets the parameters of this estimator.
- 1) Import Classification Tree Learning System from scikit-learn :
- Example(s):
- Counter-Example(s):
- See: Decision Tree, Classification System, Regularization Task, Ridge Regression Task, Kernel-based Classification Algorithm.
References
2017a
- (Scikit-Learn, 2017A) ⇒ http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html Retrieved:2017-10-22
- QUOTE:
class sklearn.tree.DecisionTreeClassifier(criterion=’gini’, splitter=’best’, max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, min_impurity_split=None, class_weight=None, presort=False)
A decision tree classifier.
Read more in the User Guide.
- QUOTE:
2017b
- (Scikit-Learn, 2017B) ⇒ http://scikit-learn.org/stable/modules/tree.html#classification
- QUOTE: DecisionTreeClassifier is a class capable of performing multi-class classification on a dataset.
As with other classifiers, DecisionTreeClassifier takes as input two arrays: an array X, sparse or dense, of size
[n_samples, n_features]
holding the training samples, and an array Y of integer values, size[n_samples]
, holding the class labels for the training samples:
- QUOTE: DecisionTreeClassifier is a class capable of performing multi-class classification on a dataset.