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

Challenge - 5 Problems

🧠 Conceptual

intermediate

Understanding the output shape of a multi-class classification model

You build a neural network for a multi-class classification problem with 5 classes. The last layer uses a softmax activation. What should be the shape of the output layer?

A Five neurons with softmax activation

B A single neuron with sigmoid activation

C Five neurons with sigmoid activation

D A single neuron with softmax activation

💡 Hint

Think about how the model outputs probabilities for each class.

For multi-class classification, the output layer should have as many neurons as classes. Softmax activation converts outputs into probabilities that sum to 1.

❓ Metrics

intermediate

Choosing the right metric for multi-class classification

You trained a multi-class classifier with 4 classes. Which metric is best to evaluate overall model performance when classes are imbalanced?

A Precision for class 1 only

B Macro-averaged F1 score

C Accuracy

D Mean Squared Error

💡 Hint

Consider a metric that treats all classes equally regardless of their frequency.

Macro-averaged F1 score calculates F1 for each class and averages them, giving equal importance to all classes, which is good for imbalanced data.

❓ Predict Output

advanced

Output of softmax probabilities for multi-class prediction

What is the output of the following Python code snippet?

ML Python

import numpy as np
from scipy.special import softmax

logits = np.array([2.0, 1.0, 0.1])
probabilities = softmax(logits)
print(probabilities)

A [0.73105858 0.19661193 0.07232949]

B [0.50000000 0.30000000 0.20000000]

C [0.65900114 0.24243297 0.09856589]

D [0.33333333 0.33333333 0.33333333]

💡 Hint

Softmax converts logits to probabilities that sum to 1.

The softmax function exponentiates each logit and normalizes by the sum of all exponentials. The output sums to 1 and reflects relative confidence.

❓ Hyperparameter

advanced

Choosing the right loss function for multi-class classification

You are training a neural network for a 3-class classification problem. Which loss function should you use to train the model effectively?

A Categorical Cross-Entropy

B Binary Cross-Entropy

C Hinge Loss

D Mean Squared Error

💡 Hint

Consider a loss function designed for multi-class problems with one-hot labels.

Categorical Cross-Entropy is the standard loss for multi-class classification with one-hot encoded labels and softmax output.

🔧 Debug

expert

Identifying the cause of poor multi-class classification accuracy

You trained a multi-class classifier with 6 classes. The training accuracy is high, but test accuracy is very low. Which of the following is the most likely cause?

A Incorrect use of sigmoid activation in output layer

B Model is underfitting due to too few epochs

C Using categorical cross-entropy loss with one-hot labels

D Model is overfitting due to lack of regularization

💡 Hint

High training accuracy but low test accuracy usually means the model memorizes training data.

Overfitting happens when the model learns training data too well but fails to generalize. Lack of regularization can cause this.

Practice

1. What does multi-class classification mean in machine learning?

easy

A. Sorting data into only two groups

B. Sorting data into three or more groups

C. Predicting continuous numbers

D. Clustering data without labels

Solution

1. Step 1: Understand classification types

   Binary classification sorts data into two groups, while multi-class sorts into three or more.

2. Step 2: Match definition to options

   Sorting data into three or more groups correctly states sorting into three or more groups, which matches multi-class classification.

3. Final Answer:

   Sorting data into three or more groups -> Option B

4. Quick Check:

   Multi-class = three or more groups [OK]

Hint: Multi-class means 3+ groups, not just 2 [OK]

Common Mistakes:

• Confusing multi-class with binary classification
• Thinking multi-class predicts numbers
• Mixing classification with clustering

2. Which of the following is the correct way to specify a multi-class classification model in Python using scikit-learn?

easy

A. from sklearn.linear_model import LogisticRegression\nmodel = LogisticRegression(multi_class='multinomial', solver='lbfgs')

B. from sklearn.linear_model import LogisticRegression\nmodel = LogisticRegression(multi_class='binary')

C. from sklearn.svm import SVC\nmodel = SVC(kernel='linear', multi_class=true)

D. from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier(multi_class='multinomial')

Solution

1. Step 1: Check scikit-learn multi-class syntax

   LogisticRegression supports multi_class='multinomial' with solver='lbfgs' for multi-class tasks.

2. Step 2: Evaluate each option

   from sklearn.linear_model import LogisticRegression\nmodel = LogisticRegression(multi_class='multinomial', solver='lbfgs') uses correct parameters. from sklearn.linear_model import LogisticRegression\nmodel = LogisticRegression(multi_class='binary') wrongly uses 'binary'. from sklearn.svm import SVC\nmodel = SVC(kernel='linear', multi_class=true)'s SVC does not have multi_class parameter. from sklearn.tree import DecisionTreeClassifier\nmodel = DecisionTreeClassifier(multi_class='multinomial')'s DecisionTreeClassifier does not accept multi_class parameter.

3. Final Answer:

   from sklearn.linear_model import LogisticRegression model = LogisticRegression(multi_class='multinomial', solver='lbfgs') -> Option A

4. Quick Check:

   LogisticRegression multi_class='multinomial' is correct [OK]

Hint: Use multi_class='multinomial' with LogisticRegression [OK]

Common Mistakes:

• Using multi_class='binary' for multi-class tasks
• Passing multi_class to models that don't accept it
• Forgetting to set solver='lbfgs' with multinomial

3. Given the following code, what will be the shape of the predicted output array?

from sklearn.datasets import load_iris
from sklearn.linear_model import LogisticRegression

iris = load_iris()
X, y = iris.data, iris.target
model = LogisticRegression(multi_class='multinomial', solver='lbfgs', max_iter=200)
model.fit(X, y)
predictions = model.predict(X)

medium

A. (3, 150)

B. (150, 3)

C. (150,)

D. (150, 1)

Solution

1. Step 1: Understand predict output shape

   For multi-class classification, predict returns a 1D array of class labels, one per sample.

2. Step 2: Check input data size

   iris dataset has 150 samples, so predictions shape is (150,)

3. Final Answer:

   (150,) -> Option C

4. Quick Check:

   Predict output shape = (number of samples,) [OK]

Hint: Predict returns 1D array of labels, length = number of samples [OK]

Common Mistakes:

• Expecting predict to return probabilities shape
• Confusing predict with predict_proba output
• Assuming output is 2D array always

4. You trained a multi-class classifier but it throws this error: ValueError: Unknown label type: 'continuous'. What is the most likely cause?

medium

A. The training data has too few samples

B. The model does not support multi-class classification

C. The input features have missing values

D. The target labels are continuous numbers instead of discrete classes

Solution

1. Step 1: Analyze error message

   ValueError about 'continuous' label type means labels are not discrete classes but continuous numbers.

2. Step 2: Match cause to options

   The target labels are continuous numbers instead of discrete classes correctly identifies continuous labels as cause. Other options do not relate to label type error.

3. Final Answer:

   The target labels are continuous numbers instead of discrete classes -> Option D

4. Quick Check:

   Continuous labels cause 'Unknown label type' error [OK]

Hint: Check if labels are discrete classes, not continuous numbers [OK]

Common Mistakes:

• Ignoring label type and focusing on features
• Assuming model limitation causes this error
• Not verifying label data format

5. You want to improve a multi-class classification model's performance on an imbalanced dataset with 5 classes. Which approach is best to try first?

hard

A. Use class weights to give more importance to minority classes during training

B. Reduce the number of classes to 2 by merging some classes

C. Increase the learning rate to speed up training

D. Remove samples from majority classes to balance dataset

Solution

1. Step 1: Understand imbalance problem

   Imbalanced classes cause model to favor majority classes, hurting minority class accuracy.

2. Step 2: Evaluate options for imbalance handling

   Using class weights (Use class weights to give more importance to minority classes during training) helps model focus on minority classes without losing data. Reducing classes (B) changes problem scope. Increasing learning rate (A) may harm training. Removing samples (D) loses valuable data.

3. Final Answer:

   Use class weights to give more importance to minority classes during training -> Option A

4. Quick Check:

   Class weights help handle imbalance best [OK]

Hint: Apply class weights to balance learning on imbalanced classes [OK]

Common Mistakes:

• Merging classes loses important distinctions
• Increasing learning rate can cause unstable training
• Removing data wastes valuable information