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)

Source: scikit-learn.org

Skit learn decision

tree.plot_tree(clf)

Source: scikit-learn.org

Skit learn decision

from sklearn import tree
X = [[0, 0], [1, 1]]
Y = [0, 1]
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, Y)

Source: scikit-learn.org

Skit learn decision

import graphviz 
dot_data = tree.export_graphviz(clf, out_file=None) 
graph = graphviz.Source(dot_data) 
graph.render("iris")

Source: scikit-learn.org

Skit learn decision

clf.predict([[2., 2.]])

Source: scikit-learn.org

Skit learn decision

from sklearn.datasets import load_iris
from sklearn import tree
X, y = load_iris(return_X_y=True)
clf = tree.DecisionTreeClassifier()
clf = clf.fit(X, y)

Source: scikit-learn.org

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