1. Which of the following methods is used to evaluate the performance of a regression
model?
A. Precision
B. Mean Squared Error (MSE)
C. F1-score
D. Accuracy
Answer: b) Mean Squared Error (MSE)
Rationale: MSE is commonly used to evaluate regression models by measuring the
average squared differences between the predicted and actual values.
2. Which of the following techniques can be used for dimensionality reduction?
A. K-nearest neighbors
B. Principal Component Analysis (PCA)
C. Decision Trees
D. Logistic Regression
Answer: b) Principal Component Analysis (PCA)
,Rationale: PCA is a method for reducing the number of features in a dataset while
preserving as much variance as possible, making it an effective technique for
dimensionality reduction.
3. Which of the following is the primary purpose of feature scaling in machine
learning?
A. To normalize the range of data features
B. To increase the number of features in the dataset
C. To reduce the dimensionality of the data
D. To prevent overfitting
Answer: a) To normalize the range of data features
Rationale: Feature scaling standardizes or normalizes the features to bring them into a
similar range, which helps improve the performance of many machine learning
algorithms.
4. Which of the following is true about "k-fold cross-validation"?
A. It splits the dataset into two parts: training and testing
B. It splits the data into 'k' subsets, training the model on 'k-1' subsets and testing on
the remaining subset
, C. It uses a single training set to evaluate the model performance multiple times
D. It is primarily used for classification problems only
Answer: b) It splits the data into 'k' subsets, training the model on 'k-1' subsets and
testing on the remaining subset
Rationale: K-fold cross-validation helps evaluate model performance by repeatedly
training and testing on different subsets of the data.
5. Which of the following is NOT a type of neural network architecture?
A. Convolutional Neural Networks (CNN)
B. Recurrent Neural Networks (RNN)
C. Naive Bayes
D. Feedforward Neural Networks (FNN)
Answer: c) Naive Bayes
Rationale: Naive Bayes is a probabilistic classifier, not a type of neural network
architecture.
6. What is the "elbow method" used for in machine learning?
A. To find the optimal number of clusters in a clustering algorithm
B. To determine the best value for the learning rate
model?
A. Precision
B. Mean Squared Error (MSE)
C. F1-score
D. Accuracy
Answer: b) Mean Squared Error (MSE)
Rationale: MSE is commonly used to evaluate regression models by measuring the
average squared differences between the predicted and actual values.
2. Which of the following techniques can be used for dimensionality reduction?
A. K-nearest neighbors
B. Principal Component Analysis (PCA)
C. Decision Trees
D. Logistic Regression
Answer: b) Principal Component Analysis (PCA)
,Rationale: PCA is a method for reducing the number of features in a dataset while
preserving as much variance as possible, making it an effective technique for
dimensionality reduction.
3. Which of the following is the primary purpose of feature scaling in machine
learning?
A. To normalize the range of data features
B. To increase the number of features in the dataset
C. To reduce the dimensionality of the data
D. To prevent overfitting
Answer: a) To normalize the range of data features
Rationale: Feature scaling standardizes or normalizes the features to bring them into a
similar range, which helps improve the performance of many machine learning
algorithms.
4. Which of the following is true about "k-fold cross-validation"?
A. It splits the dataset into two parts: training and testing
B. It splits the data into 'k' subsets, training the model on 'k-1' subsets and testing on
the remaining subset
, C. It uses a single training set to evaluate the model performance multiple times
D. It is primarily used for classification problems only
Answer: b) It splits the data into 'k' subsets, training the model on 'k-1' subsets and
testing on the remaining subset
Rationale: K-fold cross-validation helps evaluate model performance by repeatedly
training and testing on different subsets of the data.
5. Which of the following is NOT a type of neural network architecture?
A. Convolutional Neural Networks (CNN)
B. Recurrent Neural Networks (RNN)
C. Naive Bayes
D. Feedforward Neural Networks (FNN)
Answer: c) Naive Bayes
Rationale: Naive Bayes is a probabilistic classifier, not a type of neural network
architecture.
6. What is the "elbow method" used for in machine learning?
A. To find the optimal number of clusters in a clustering algorithm
B. To determine the best value for the learning rate
