Decision tree

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

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)

-2

Source: scikit-learn.org

Decision trees

Training Data Set Accuracy:  0.9610705596107056
Training Data F1 Score  0.972027972027972
Validation Mean F1 Score:  0.6348494236272646
Validation Mean Accuracy:  0.7030561269468117

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