BASICS

1. Which of the following is an example of supervised learning?

• A) Clustering customers

• B) Predicting house prices based on features

• C) Dimensionality reduction

• D) Principal Component Analysis

2. In machine learning, overfitting occurs when...

• A) The model performs well on new, unseen data
  

• B) The model performs well on training data but poorly on test data

• C) The model is too simple to capture the pattern

• D) The learning rate is too high

Which algorithm is used for classification and regression tasks?

• A) K-Means

• B) Decision Tree

• C) Apriori

• D) PCA

What is the role of an activation function in a neural network?

• To normalize data

• To introduce non-linearity

• To compute the loss

• To reduce dimensionality

True / False :Gradient Descent is used to minimize the loss function.

True/False :A high variance model is likely to underfit the data.

Which technique is commonly used to prevent overfitting in machine learning models?

• D) Gradient descent

• C) Data augmentation

• A) Cross-validation

• B) Feature scaling

What is the purpose of the loss function in a machine learning model?

• A) To measure the accuracy of the model

• B) To optimize the model parameters

• C) To evaluate the model's performance

• D) To quantify the difference between predicted and actual values

Which of the following is a common method for feature selection?

• A) Recursive Feature Elimination

• B) K-Means Clustering

• C) Principal Component Analysis

• D) Linear Regression

What is the purpose of regularization in machine learning?

• D) To improve data quality

• C) To prevent overfitting

• A) To increase the model complexity

• B) To reduce the training time