Multiclass SVM 2/5 points (graded) In fact, sklearn already implements a multiclass SVM with a one-vs-rest...

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Multiclass SVM 2/5 points (graded) In fact, sklearn already implements a multiclass SVM with a one-vs-rest strategy. Use LinearSvc to build a multiclass SVM model Available Functions: You have access to the sklearn's implementation of the linear SVM as LinearSVC; No need to import anything. 4 5 6 7 8 9 10 11 Args: train_x - (n, d) NumPy array (n datapoints each with d features) train_y (n, ) NumPy array containing the labels (int) for each training data point test_x - (m, d) NumPy array (m datapoints each with d features) Returns: pred_test_y- (m,) NumPy array containing the labels (int) for each test data point svm = Linear SVC (random_state=0, tol=1e-5) 12 13 svm.fit(train_x, train_y) 14 pred_test_y= svm.predict(test_x) 15 return pred_test_y 16 17 18 Press ESC then TAB or click outside of the code editor to exit raise NotImplementedError Incorrect Multiclass SVM 2/5 points (graded) In fact, sklearn already implements a multiclass SVM with a one-vs-rest strategy. Use LinearSvc to build a multiclass SVM model Available Functions: You have access to the sklearn's implementation of the linear SVM as LinearSVC; No need to import anything. 4 5 6 7 8 9 10 11 Args: train_x - (n, d) NumPy array (n datapoints each with d features) train_y (n, ) NumPy array containing the labels (int) for each training data point test_x - (m, d) NumPy array (m datapoints each with d features) Returns: pred_test_y- (m,) NumPy array containing the labels (int) for each test data point svm = Linear SVC (random_state=0, tol=1e-5) 12 13 svm.fit(train_x, train_y) 14 pred_test_y= svm.predict(test_x) 15 return pred_test_y 16 17 18 Press ESC then TAB or click outside of the code editor to exit raise NotImplementedError Incorrect

Answer rating: 100% (QA)

Step 1 Algorithm Import the necessary libraries from sklearnsvm import LinearSVC for the LinearSVC classifier import numpy as np for numerical operations using NumPy Define the multiclasssvm function ... View the full answer