Multi-label classification in ML Python - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is multi-label classification?

Multi-label classification is a type of machine learning task where each example can belong to more than one class or category at the same time. For example, a photo can have both 'cat' and 'dog' labels.

beginner

How does multi-label classification differ from multi-class classification?

In multi-class classification, each example belongs to only one class out of many. In multi-label classification, each example can belong to multiple classes simultaneously.

intermediate

Name a common method to handle multi-label classification.

One common method is to use a separate binary classifier for each label. This means the model predicts yes/no for each label independently.

intermediate

What metric can be used to evaluate multi-label classification models?

Metrics like Hamming Loss, F1-score (micro and macro), and Jaccard Index are used to evaluate multi-label classification models because they consider multiple labels per example.

beginner

Why is multi-label classification important in real life?

Because many real-world problems involve items that belong to multiple categories, like tagging photos, music genres, or medical diagnoses, multi-label classification helps models understand and predict these complex cases.

In multi-label classification, an example can have:

A Multiple labels at the same time

B Only one label

C No labels

D Labels that are mutually exclusive

Multi-label classification allows each example to have multiple labels simultaneously.

Which metric is suitable for evaluating multi-label classification?

A Accuracy for single label

B Mean Squared Error

C Hamming Loss

D BLEU score

Hamming Loss measures how many labels are incorrectly predicted in multi-label classification.

A simple way to build a multi-label classifier is to:

A Train one multi-class classifier

B Train one binary classifier per label

C Use clustering algorithms

D Ignore label dependencies

Training one binary classifier per label is a common approach for multi-label classification.

Which of these is NOT true about multi-label classification?

A Labels are mutually exclusive

B Each example can belong to multiple classes

C It is used in tagging images

D It requires special evaluation metrics

In multi-label classification, labels are not mutually exclusive; examples can have multiple labels.

Multi-label classification is useful when:

A There are no categories

B Items belong to exactly one category

C Categories are hierarchical only

D Items can belong to several categories

Multi-label classification handles cases where items belong to multiple categories at once.

Explain what multi-label classification is and how it differs from multi-class classification.

Think about how many labels an example can have.

Describe one common method to build a multi-label classification model and name a metric to evaluate it.

Consider how to predict each label separately.

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