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I # YOUR CODE HERE def train_DT(X,Y): clf = DecisionTreeclassifier(random_state = 0, max_depth = 2) clf = clf.fit(X,Y) return clf I assert callable(train_DT) 2h) Classification #1: Using Only Subject Let's try to classify the email conversation using only the subject field of the dataframe only. Using the function train_DT() , train a decision tree classifier on subject_train_X (as your predictor) and category_train_Y (as your outcome) and save the model as subject_clf . I # YOUR CODE HERE raise NotImplementedError() I assert isinstance(subject_clf, DecisionTreeclassifier) assert hasattr(subject_clf, "predict") Now we will use the function classification_report to print out the performance of the classifier on the training set: I # Your classifier should observe an accuracy of around 96%. subject_predicted_train_Y = subject_clf.predict(subject_train_X) print(classification_report(category_train_Y, subject_predicted_train_Y))

I # YOUR CODE HERE def train_DT(X,Y): clf = DecisionTreeclassifier(random_state = 0, max_depth = 2) clf = clf.fit(X,Y) return clf I assert callable(train_DT) 2h) Classification #1: Using Only Subject Let's try to classify the email conversation using only the subject field of the dataframe only. Using the function train_DT() , train a decision tree classifier on subject_train_X (as your predictor) and category_train_Y (as your outcome) and save the model as subject_clf . I # YOUR CODE HERE raise NotImplementedError() I assert isinstance(subject_clf, DecisionTreeclassifier) assert hasattr(subject_clf, "predict") Now we will use the function classification_report to print out the performance of the classifier on the training set: I # Your classifier should observe an accuracy of around 96%. subject_predicted_train_Y = subject_clf.predict(subject_train_X) print(classification_report(category_train_Y, subject_predicted_train_Y))

Database System Concepts
Database System Concepts
7th Edition
ISBN:9780078022159
Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan
Chapter1: Introduction
Section: Chapter Questions
Problem 1PE
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Using Python

I # YOUR CODE HERE def train_DT(X,Y): clf = DecisionTreeclassifier(random_state = 0, max_depth = 2) clf = clf.fit(X,Y) %3D return clf I assert callable(train_DT) 2h) Classification #1: Using Only Subject Let's try to classify the email conversation using only the subject field of the dataframe only. Using the function train_DT() , train a decision tree classifier on subject_train_X (as your predictor) and category_train_Y (as your outcome) and save the model as subject_clf . I # YOUR CODE HERE raise NotImplementedError() I assert isinstance(subject_clf, DecisionTreeclassifier) assert hasattr(subject_clf, "predict") Now we will use the function classification_report to print out the performance of the classifier on the training set: I # Your classifier should observe an accuracy of around 96%. subject_predicted_train_Y print(classification_report(category_train_Y, subject_predicted_train_Y)) subject_clf.predict(subject_train_X)
Transcribed Image Text:I # YOUR CODE HERE def train_DT(X,Y): clf = DecisionTreeclassifier(random_state = 0, max_depth = 2) clf = clf.fit(X,Y) %3D return clf I assert callable(train_DT) 2h) Classification #1: Using Only Subject Let's try to classify the email conversation using only the subject field of the dataframe only. Using the function train_DT() , train a decision tree classifier on subject_train_X (as your predictor) and category_train_Y (as your outcome) and save the model as subject_clf . I # YOUR CODE HERE raise NotImplementedError() I assert isinstance(subject_clf, DecisionTreeclassifier) assert hasattr(subject_clf, "predict") Now we will use the function classification_report to print out the performance of the classifier on the training set: I # Your classifier should observe an accuracy of around 96%. subject_predicted_train_Y print(classification_report(category_train_Y, subject_predicted_train_Y)) subject_clf.predict(subject_train_X)
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