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)

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

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.

Practice

1. What is the main difference between multi-label classification and multi-class classification?

easy

A. Multi-label classification uses regression, multi-class uses classification.

B. Multi-label classification assigns only one label, multi-class assigns multiple labels.

C. Multi-label classification is used only for images, multi-class for text.

D. Multi-label classification assigns multiple labels to one example, multi-class assigns only one.

Solution

1. Step 1: Understand multi-label classification

   Multi-label classification means each example can have more than one correct label at the same time.

2. Step 2: Compare with multi-class classification

   Multi-class classification means each example can have only one label from many possible classes.

3. Final Answer:

   Multi-label classification assigns multiple labels to one example, multi-class assigns only one. -> Option D

4. Quick Check:

   Multi-label = multiple labels, multi-class = single label [OK]

Hint: Remember: multi-label means many labels per example [OK]

Common Mistakes:

• Confusing multi-label with multi-class
• Thinking multi-label assigns only one label
• Mixing up classification with regression
• Assuming multi-label is only for images

2. Which of the following is a correct way to represent labels for multi-label classification in Python?

easy

A. labels = [0, 1, 2]

B. labels = [[1, 0, 1], [0, 1, 0]]

C. labels = 'cat,dog,bird'

D. labels = 3

Solution

1. Step 1: Understand label representation for multi-label

   Multi-label classification uses a list or array where each position represents a label, with 1 or 0 indicating presence or absence.

2. Step 2: Check options for correct format

   labels = [[1, 0, 1], [0, 1, 0]] shows a list of lists with 1s and 0s, correctly representing multiple labels per example.

3. Final Answer:

   labels = [[1, 0, 1], [0, 1, 0]] -> Option B

4. Quick Check:

   Multi-label uses binary vectors per example [OK]

Hint: Use binary lists to show multiple labels [OK]

Common Mistakes:

• Using a single integer for labels
• Using a string instead of list
• Using a flat list without nested structure
• Confusing multi-class label format with multi-label

3. Given this Python code snippet for multi-label classification predictions:

import numpy as np
preds = np.array([[0.8, 0.1, 0.6], [0.3, 0.7, 0.2]])
threshold = 0.5
binary_preds = (preds > threshold).astype(int)
print(binary_preds)

What is the output?

medium

A. [[1 1 1]
[0 0 0]]

B. [[0 1 0]
[1 0 1]]

C. [[1 0 1]
[0 1 0]]

D. [[0 0 0]
[1 1 1]]

Solution

1. Step 1: Apply threshold to predictions

   Compare each value in preds with 0.5: values > 0.5 become 1, else 0.

2. Step 2: Convert boolean to int and print

   First row: 0.8>0.5=1, 0.1>0.5=0, 0.6>0.5=1; Second row: 0.3>0.5=0, 0.7>0.5=1, 0.2>0.5=0.

3. Final Answer:

   [[1 0 1] [0 1 0]] -> Option C

4. Quick Check:

   Thresholding preds > 0.5 = binary labels [OK]

Hint: Compare each prediction to threshold for binary output [OK]

Common Mistakes:

• Confusing > with >=
• Not converting boolean to int
• Mixing rows and columns in output
• Using wrong threshold value

4. You trained a multi-label model but it always predicts only one label per example. What is the most likely cause?

medium

A. Using softmax activation instead of sigmoid in the output layer

B. Using sigmoid activation instead of softmax in the output layer

C. Using binary cross-entropy loss

D. Using a threshold of 0.1 for predictions

Solution

1. Step 1: Understand output activations for multi-label

   Multi-label models use sigmoid activation to allow independent probabilities per label.

2. Step 2: Identify problem with softmax

   Softmax forces probabilities to sum to 1, so only one label gets high probability, limiting multi-label predictions.

3. Final Answer:

   Using softmax activation instead of sigmoid in the output layer -> Option A

4. Quick Check:

   Softmax limits to one label, sigmoid allows many [OK]

Hint: Use sigmoid for multi-label, softmax for single-label [OK]

Common Mistakes:

• Confusing softmax and sigmoid activations
• Ignoring loss function compatibility
• Setting threshold too low or high
• Assuming threshold fixes activation issues

5. You have a dataset where each image can have multiple tags like 'beach', 'sunset', and 'people'. You want to build a multi-label classifier. Which metric is best to evaluate your model's performance?

hard

A. Precision, Recall, and F1-score calculated per label and averaged

B. Accuracy (percentage of exact matches of all labels)

C. Mean Squared Error

D. Confusion matrix for single-label classification

Solution

1. Step 1: Understand evaluation needs for multi-label

   Exact match accuracy is too strict because all labels must match perfectly, which is rare.

2. Step 2: Choose suitable metrics

   Precision, Recall, and F1-score per label, then averaged, give a balanced view of performance on each label.

3. Final Answer:

   Precision, Recall, and F1-score calculated per label and averaged -> Option A

4. Quick Check:

   Use per-label metrics averaged for multi-label evaluation [OK]

Hint: Use per-label precision/recall for multi-label metrics [OK]

Common Mistakes:

• Using strict accuracy that ignores partial matches
• Using regression metrics like MSE
• Using single-label confusion matrix
• Ignoring label imbalance in metrics