Multi-label classification in ML Python - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the correct multi-label classifier from scikit-learn.

ML Python

from sklearn.multioutput import [1]

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

AMultiOutputClassifier

BRandomForestClassifier

CLogisticRegression

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Importing a single-label classifier instead of a multi-label one.

Confusing RandomForestClassifier with MultiOutputClassifier.

The MultiOutputClassifier is used for multi-label classification in scikit-learn.

2fill in blank

medium

Complete the code to create a multi-label classifier using a decision tree as the base estimator.

ML Python

from sklearn.tree import DecisionTreeClassifier
from sklearn.multioutput import MultiOutputClassifier

model = MultiOutputClassifier([1]())

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

ADecisionTreeClassifier

BRandomForestClassifier

CLogisticRegression

DKNeighborsClassifier

Attempts:

3 left

💡 Hint

Common Mistakes

Using a classifier not imported or unrelated to the task.

Forgetting to instantiate the classifier with parentheses.

The base estimator for MultiOutputClassifier here should be DecisionTreeClassifier.

3fill in blank

hard

Fix the error in the code to correctly fit the multi-label classifier on data X and labels Y.

ML Python

model = MultiOutputClassifier(DecisionTreeClassifier())
model.[1](X, Y)

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

Atransform

Bpredict

Cfit

Dscore

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model.

Using transform which is not applicable here.

To train the model, use the fit method with features X and labels Y.

4fill in blank

hard

Fill both blanks to compute the accuracy score for multi-label classification.

ML Python

from sklearn.metrics import [1]

score = [2](Y_true, Y_pred)

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Aaccuracy_score

Bf1_score

Croc_auc_score

Dmean_squared_error

Attempts:

3 left

💡 Hint

Common Mistakes

Using regression metrics like mean_squared_error for classification.

Using roc_auc_score which is for binary or multilabel-indicator but needs special handling.

Use accuracy_score from sklearn.metrics to compute accuracy.

5fill in blank

hard

Fill all three blanks to create a multi-label classifier with logistic regression, fit it, and predict labels.

ML Python

from sklearn.linear_model import [1]
from sklearn.multioutput import MultiOutputClassifier

model = MultiOutputClassifier([2]())
model.[3](X_train, Y_train)
Y_pred = model.predict(X_test)

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

ALogisticRegression

BDecisionTreeClassifier

Cfit

Dpredict

Attempts:

3 left

💡 Hint

Common Mistakes

Using predict instead of fit to train the model.

Using a different base estimator than imported.

Import LogisticRegression, use it as base estimator, then fit the model.