Use Python 2g) Decision Tree ClassificationDefine a function train_DT() which takes as input x and Y and return s a Decision Tree model trained on the givenX , Y .Inputs:X: np.ndarray, training samples (predictors),Y: np.ndarray, training labels (outcomes),Output: a trained classifier clfHint: There are 2 steps involved in this function:•Initializing an Decision Tree classifier: clf =Training the classifier: clf.fit(X, Y)DecisionTreeClassifier(...)I # YOUR CODE HEREraise NotImplementedError()I assert callable(train_DT)2h) Classification #1: Using Only SubjectLet'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) andcategory_train_Y (as your outcome) and save the model as subject_clf.I # YOUR CODE HEREraise 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_Yprint(classification_report(category_train_Y, subject_predicted_train_Y))subject_clf.predict(subject_train_X)And now, let's check the performance of the trained classifier on the test set:I # You should observe an accuracy of around 79%.subject_predicted_test_Yprint(classification_report(category_test_Y, subject_predicted_test_Y))subject_clf.predict(subject_test_X)I assert subject_predicted_train_Y.shapeassert subject_predicted_test_Y.shape ==(14984,)(3746,)==precision, recall,assert np.isclose(precision[0], 0.99, 0.05)= precision_recall_fscore_support(category_train_Y, subject_predicted_train_

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2g) Decision Tree Classification Define a function train_DT() which takes as input x and Y and return s a Decision Tree model trained on the given X , Y . Inputs: • X: np.ndarray, training samples (predictors), • Y: np.ndarray, training labels (outcomes), Output: a trained classifier clf Hint: There are 2 steps involved in this function: Initializing an Decision Tree classifier: clf = DecisionTreeClassifier(...) • Training the classifier: clf.fit(X, Y) I # YOUR CODE HERE raise NotImplementedError(). I assert callable(train_DT)

2g) Decision Tree Classification Define a function train_DT() which takes as input x and Y and return s a Decision Tree model trained on the given X , Y . Inputs: • X: np.ndarray, training samples (predictors), • Y: np.ndarray, training labels (outcomes), Output: a trained classifier clf Hint: There are 2 steps involved in this function: Initializing an Decision Tree classifier: clf = DecisionTreeClassifier(...) • Training the classifier: clf.fit(X, Y) I # YOUR CODE HERE raise NotImplementedError(). I assert callable(train_DT)

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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Develop a simple table of examples in some domain, such as classifying plants by species, and trace the construction of a decision tree by the ID3 algorithm. Construct a decision tree using py
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