decisiontreeclassifier sklearn
from sklearn.tree import DecisionTreeClassifierdecisiontreeclassifier sklearn
from sklearn.tree import DecisionTreeClassifierdecision tree classifier sklearn
from sklearn.datasets import load_iris
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(random_state=0)
iris = load_iris()
cross_val_score(clf, iris.data, iris.target, cv=10)skit learn decision
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import export_text
iris = load_iris()
decision_tree = DecisionTreeClassifier(random_state=0, max_depth=2)
decision_tree = decision_tree.fit(iris.data, iris.target)
r = export_text(decision_tree, feature_names=iris['feature_names'])
print(r)scikit decision tree classifier gini criterion
from sklearn.tree import DecisionTreeClassifier
from sklearn import metrics
# Max depth Decision tree classifier using gini criterion
clf_gini_max = DecisionTreeClassifier(random_state=50, criterion='gini', max_depth=None)
clf_gini_max = clf_gini_max.fit(X_train,Y_train)
Y_pred = clf_gini_max.predict(X_test)
training_accuracy = clf_gini_max.score(X_train,Y_train)
testing_accuracy = clf_gini_max.score(X_test,Y_test)
print(training_accuracy)
print(testing_accuracy)skit learn decision
clf.predict([[2., 2.]])Copyright © 2021 Codeinu
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