Question 1

What is the primary objective of cross-entropy loss in classification tasks?

Options :

A : Maximize Prediction Accuracy

B : Minimize Prediction Error

C : Minimize Prediction Variance

D : Maximize Prediction Confidence

E : Measure Prediction Uncertainty

F : Improve Model Stability

Answer: C

Question 2

In which phase of the machine learning process are hyperparameters tuned to optimize model performance?

Options :

A : Model Deployment

B : Model Evaluation

C : Model Training

D : Feature Engineering

E : Hyperparameter Tuning

F : Data Preprocessing

Answer: E

Question 3

What is the primary purpose of the softmax activation function in the output layer of a neural network for multi-class classification?

Options :

A : Reduce Model Complexity

B : Introduce Non-Linearity

C : Scale Input Features

D : Speed Up Convergence

E : Normalize Data

F : Compute Class Probabilities

Answer: F

Question 4

What is the main purpose of using activation functions in neural networks?

Options :

A : To normalize input data

B : To introduce non-linearity

C : To reduce dimensionality

D : To cluster data

Answer: B

Question 5

Which of the following is NOT a common technique for improving the performance of classification models?

Options :

A : Feature Engineering

B : Ensemble Learning

C : Hyperparameter Tuning

D : Regularization

E : Gradient Descent

F : Dimensionality Reduction

Answer: E

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