Question 1

What is the difference between supervised and unsupervised learning?

Accepted Answer

Supervised learning uses labeled data, while unsupervised learning works with unlabeled data.

Question 2

Define overfitting in machine learning.

Accepted Answer

Overfitting occurs when a model learns the training data too well, capturing noise and failing to generalize.

Question 3

What is a confusion matrix?

Accepted Answer

A confusion matrix is a table used to evaluate the performance of a classification model.

Question 4

Explain the bias-variance trade-off.

Accepted Answer

It is the balance between a model's simplicity (bias) and its flexibility (variance) to minimize total error.

Question 5

What is the purpose of a cost function?

Accepted Answer

A cost function measures the error between predicted values and actual target values.

Question 6

Define the term 'epoch' in neural networks.

Accepted Answer

An epoch represents one complete pass of the entire training dataset through the neural network.

Question 7

What is the kernel trick in SVM?

Accepted Answer

The kernel trick transforms data into a higher-dimensional space to make it linearly separable.

Question 8

Mention two techniques to handle missing data.

Accepted Answer

Missing data can be handled using imputation or deletion.

Question 9

What is the difference between classification and regression?

Accepted Answer

Classification predicts categorical labels, while regression predicts continuous numerical values.

Question 10

Name any two evaluation metrics for classification models.

Accepted Answer

Common classification metrics include Accuracy, Precision, Recall, and F1-Score.

Question 11

Explain Logistic Regression. How does it differ from Linear Regression?

Accepted Answer

Logistic Regression is used for binary classification, whereas Linear Regression is used for continuous prediction.

Question 12

Describe the steps involved in the K-Means clustering algorithm.

Accepted Answer

K-Means divides data into K clusters by minimizing the variance within each cluster iteratively.

Question 13

What is cross-validation? Explain k-fold cross-validation.

Accepted Answer

Cross-validation evaluates a model's performance on unseen data. K-fold splits data into K equal subsets.

Question 14

How does a Support Vector Machine (SVM) handle non-linearly separable data?

Accepted Answer

SVM uses the kernel trick to map data to a higher-dimensional space where a linear hyperplane can separate the classes.

Question 15

Explain the Random Forest algorithm. What are its advantages?

Accepted Answer

Random Forest is an ensemble method that builds multiple decision trees and merges them for a more accurate and stable prediction.

Question 16

Discuss the concept of Gradient Descent with a suitable example.

Accepted Answer

Gradient Descent is an optimization algorithm used to minimize the cost function by iteratively updating model parameters.

Question 17

What is PCA? Explain its significance in dimensionality reduction.

Accepted Answer

Principal Component Analysis (PCA) transforms correlated variables into a smaller number of uncorrelated principal components.

Question 18

Explain the Backpropagation algorithm in Artificial Neural Networks with a de…

Accepted Answer

Backpropagation systematically updates neural network weights by calculating the gradient of the loss function using the chain rule.

Question 19

Discuss Decision Trees in detail. Explain the concepts of Entropy, Informatio…

Accepted Answer

Decision Trees split data based on feature values to maximize information gain and minimize entropy.

Question 20

Provide a comprehensive explanation of Ensemble Learning techniques. Compare …

Accepted Answer

Ensemble learning combines multiple weak learners to form a strong learner. Bagging reduces variance, while Boosting reduces bias.

Question 21

Elaborate on Support Vector Machines (SVM). Explain margins, hyperplanes, and…

Accepted Answer

SVM finds the optimal hyperplane that maximizes the margin between classes using support vectors.

Question 22

Explain Principal Component Analysis (PCA) algorithm step-by-step. How does i…

Accepted Answer

PCA performs eigenvalue decomposition on the covariance matrix to find the directions of maximum variance.

Machine Learning