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)skitlearn decision tree
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)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)google scikit learn decision tree
>>> from sklearn import tree
>>> X = [[0, 0], [1, 1]]
>>> Y = [0, 1]
>>> clf = tree.DecisionTreeClassifier()
>>> clf = clf.fit(X, Y)scikit learn decision tree
from sklearn import tree
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)skit learn decision
import graphviz
dot_data = tree.export_graphviz(clf, out_file=None)
graph = graphviz.Source(dot_data)
graph.render("iris")Copyright © 2021 Codeinu
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