Multi-class classification in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the correct model for multi-class classification.

ML Python

from sklearn.linear_model import [1]

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AKNeighborsClassifier

BLinearRegression

CLogisticRegression

DDecisionTreeRegressor

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing LinearRegression which is for regression tasks.

Using DecisionTreeRegressor which is not a classifier.

LogisticRegression supports multi-class classification with the 'multinomial' option.

2fill in blank

medium

Complete the code to create a logistic regression model for multi-class classification.

ML Python

model = LogisticRegression(multi_class=[1], solver='lbfgs')

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A'auto'

B'binary'

C'ovr'

D'multinomial'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'binary' which only works for two classes.

Using 'auto' which defaults to 'ovr' for multi-class.

The 'multinomial' option enables true multi-class classification.

3fill in blank

hard

Fix the error in the code to correctly fit the multi-class model.

ML Python

model.fit(X_train, [1])

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Ay_train

BX_test

Cy_test

DX_train

Attempts:

3 left

💡 Hint

Common Mistakes

Passing X_test or y_test which are for evaluation, not training.

Passing features instead of labels.

The model must be trained with the training labels y_train, not test data or features.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps each class to its predicted probability.

ML Python

prob_dict = {cls: [1][i] for i, cls in enumerate([2])}

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Aprobs

Bclasses

Cpredictions

Dlabels

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'predictions' which are class labels, not probabilities.

Using 'labels' which is ambiguous.

We use 'probs' to get probabilities and 'classes' to get class labels.

5fill in blank

hard

Fill all three blanks to compute accuracy score for multi-class classification.

ML Python

from sklearn.metrics import [1]

accuracy = [2]([3], y_pred)

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Aaccuracy_score

By_true

Caccuracy

Dprecision_score

Attempts:

3 left

💡 Hint

Common Mistakes

Using precision_score which measures precision, not accuracy.

Swapping true and predicted labels.

We import accuracy_score and use it with true labels y_true and predicted labels y_pred.