Support Vector Machine (SVM) in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the SVM classifier from scikit-learn.

ML Python

from sklearn.svm import [1]

Drag options to blanks, or click blank then click option'

ASVC

BSVR

CSVMClassifier

DLinearSVC

Attempts:

3 left

2fill in blank

medium

Complete the code to create an SVM model with a linear kernel.

ML Python

model = SVC(kernel=[1])

Drag options to blanks, or click blank then click option'

A'rbf'

B'poly'

C'linear'

D'sigmoid'

Attempts:

3 left

3fill in blank

hard

Fix the error in the code to train the SVM model on data X and labels y.

ML Python

model = SVC()
model.[1](X, y)

Drag options to blanks, or click blank then click option'

Atrain

Btransform

Cpredict

Dfit

Attempts:

3 left

4fill in blank

hard

Fill both blanks to create a dictionary of predictions for each sample in X_test.

ML Python

predictions = {i: model.[1](X_test[[2]]) for i in range(len(X_test))}

Drag options to blanks, or click blank then click option'

Apredict

Bi

C0

DX_test

Attempts:

3 left

5fill in blank

hard

Fill all three blanks to create a dictionary of support vectors and their indices.

ML Python

support_info = {idx: vec for idx, vec in enumerate(model.[1]) if idx [2] len(model.[3])}

Drag options to blanks, or click blank then click option'

Asupport_vectors_

B<

Csupport_

D>

Attempts:

3 left