July 2017
Beginner to intermediate
442 pages
10h 8m
English
Content preview from Statistics for Machine Learning
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Decision tree classifier
The DecisionTtreeClassifier from scikit-learn has been utilized for modeling purposes, which is available in the tree submodule:
# Decision Tree Classifier >>> from sklearn.tree import DecisionTreeClassifier
The parameters selected for the DT classifier are in the following code with splitting criterion as Gini, Maximum depth as 5, minimum number of observations required for qualifying split is 2, and the minimum samples that should be present in the terminal node is 1:
>>> dt_fit = DecisionTreeClassifier(criterion="gini", max_depth=5,min_samples_split=2, min_samples_leaf=1,random_state=42) >>> dt_fit.fit(x_train,y_train) >>> print ("\nDecision Tree - Train Confusion Matrix\n\n", pd.crosstab(y_train, dt_fit.predict(x_train),rownames ...Become an O’Reilly member and get unlimited access to this title plus top books and audiobooks from O’Reilly and nearly 200 top publishers, thousands of courses curated by job role, 150+ live events each month,
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