Multi-label classification in ML Python - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding Multi-label Classification

Which statement best describes multi-label classification?

A Each instance is assigned exactly one label from multiple classes.

B Each instance can be assigned multiple labels simultaneously.

C It is a regression problem predicting continuous values.

D Labels are predicted sequentially, one after another.

💡 Hint

Think about whether an example can belong to more than one category at the same time.

Multi-label classification means each example can have multiple labels at once, unlike single-label classification where only one label is assigned.

❓ Model Choice

intermediate

Choosing a Model for Multi-label Classification

You want to build a multi-label classifier. Which model architecture is most suitable?

A A neural network with multiple output neurons using sigmoid activation.

B A decision tree that outputs one label per instance.

C A linear regression model predicting continuous values.

D A neural network with a single output neuron using softmax activation.

💡 Hint

Consider how the output layer should handle multiple labels independently.

Multi-label classification requires independent probabilities for each label, so sigmoid activation on multiple outputs is appropriate.

❓ Metrics

advanced

Evaluating Multi-label Classification Performance

Which metric is best suited to evaluate a multi-label classification model?

A Mean Squared Error between predicted and true labels.

B Accuracy calculated as (correct predictions / total samples).

C Hamming Loss measuring the fraction of wrong labels to total labels.

D R-squared score measuring variance explained by the model.

💡 Hint

Think about a metric that counts label-wise errors in multi-label settings.

Hamming Loss counts how many labels are incorrectly predicted per instance, making it suitable for multi-label tasks.

❓ Predict Output

advanced

Output of Multi-label Prediction Code

What is the output of the following Python code snippet?

ML Python

import numpy as np
from sklearn.preprocessing import MultiLabelBinarizer

labels = [['cat', 'dog'], ['dog'], ['mouse', 'cat'], []]
mlb = MultiLabelBinarizer(classes=['cat', 'dog', 'mouse'])
encoded = mlb.fit_transform(labels)
print(encoded.tolist())

A [[1, 1, 1], [0, 1, 0], [1, 0, 1], [0, 0, 0]]

B [[1, 0, 1], [0, 1, 0], [1, 1, 0], [0, 0, 0]]

C [[0, 1, 1], [1, 0, 0], [0, 1, 1], [0, 0, 0]]

D [[1, 1, 0], [0, 1, 0], [1, 0, 1], [0, 0, 0]]

💡 Hint

Check the order of classes and how MultiLabelBinarizer encodes labels.

MultiLabelBinarizer encodes each label as a binary vector in the order of classes given.

🔧 Debug

expert

Debugging Multi-label Classification Training Issue

Given this training code snippet for a multi-label classifier, what is the main cause of the error?

import torch
import torch.nn as nn

model = nn.Sequential(
    nn.Linear(10, 3),
    nn.Softmax(dim=1)
)

criterion = nn.BCEWithLogitsLoss()
inputs = torch.randn(5, 10)
targets = torch.randint(0, 2, (5, 3)).float()
outputs = model(inputs)
loss = criterion(outputs, targets)

A Using Softmax activation before BCEWithLogitsLoss causes incorrect input range.

B Targets should be integers, not floats, for BCEWithLogitsLoss.

C The input tensor shape does not match the model output shape.

D BCEWithLogitsLoss requires outputs to be probabilities between 0 and 1.

💡 Hint

Consider what BCEWithLogitsLoss expects as input and what Softmax outputs.

BCEWithLogitsLoss expects raw logits, so applying Softmax before it is incorrect and causes training issues.

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