1. Which of the following is a technique for handling class imbalance in classification
tasks?
A. Feature engineering
B. Cross-validation
C. Synthetic Minority Over-sampling Technique (SMOTE)
D. Data scaling
Answer: C) Synthetic Minority Over-sampling Technique (SMOTE)
Rationale: SMOTE is a technique to balance the classes by generating synthetic
samples for the minority class.
2. In the context of regression, what does R-squared measure?
A. The accuracy of the predictions
B. The proportion of variance in the dependent variable that is predictable from the
independent variables
C. The number of outliers in the dataset
D. The mean absolute error of the model
,Answer: B) The proportion of variance in the dependent variable that is predictable
from the independent variables
Rationale: R-squared measures how well the independent variables explain the
variability of the dependent variable.
3. Which of the following is used for dimensionality reduction?
A. K-Nearest Neighbors (KNN)
B. Principal Component Analysis (PCA)
C. Naive Bayes
D. Logistic Regression
Answer: B) Principal Component Analysis (PCA)
Rationale: PCA is a technique used to reduce the dimensionality of a dataset while
retaining most of the variance.
4. Which of the following is a type of reinforcement learning?
A. Q-learning
B. K-Nearest Neighbors
C. K-Means Clustering
D. Support Vector Regression
, Answer: A) Q-learning
Rationale: Q-learning is a type of reinforcement learning, where an agent learns to
take actions in an environment to maximize a reward.
5. Which of the following is an example of a non-linear activation function?
A. Sigmoid
B. ReLU (Rectified Linear Unit)
C. Tanh
D. All of the above
Answer: D) All of the above
Rationale: Sigmoid, ReLU, and Tanh are all non-linear activation functions used in
neural networks.
6. Which of the following metrics is used for evaluating the performance of a binary
classification model?
A. Accuracy
B. Mean Absolute Error (MAE)
C. Precision, Recall, and F1-Score
D. R-squared
tasks?
A. Feature engineering
B. Cross-validation
C. Synthetic Minority Over-sampling Technique (SMOTE)
D. Data scaling
Answer: C) Synthetic Minority Over-sampling Technique (SMOTE)
Rationale: SMOTE is a technique to balance the classes by generating synthetic
samples for the minority class.
2. In the context of regression, what does R-squared measure?
A. The accuracy of the predictions
B. The proportion of variance in the dependent variable that is predictable from the
independent variables
C. The number of outliers in the dataset
D. The mean absolute error of the model
,Answer: B) The proportion of variance in the dependent variable that is predictable
from the independent variables
Rationale: R-squared measures how well the independent variables explain the
variability of the dependent variable.
3. Which of the following is used for dimensionality reduction?
A. K-Nearest Neighbors (KNN)
B. Principal Component Analysis (PCA)
C. Naive Bayes
D. Logistic Regression
Answer: B) Principal Component Analysis (PCA)
Rationale: PCA is a technique used to reduce the dimensionality of a dataset while
retaining most of the variance.
4. Which of the following is a type of reinforcement learning?
A. Q-learning
B. K-Nearest Neighbors
C. K-Means Clustering
D. Support Vector Regression
, Answer: A) Q-learning
Rationale: Q-learning is a type of reinforcement learning, where an agent learns to
take actions in an environment to maximize a reward.
5. Which of the following is an example of a non-linear activation function?
A. Sigmoid
B. ReLU (Rectified Linear Unit)
C. Tanh
D. All of the above
Answer: D) All of the above
Rationale: Sigmoid, ReLU, and Tanh are all non-linear activation functions used in
neural networks.
6. Which of the following metrics is used for evaluating the performance of a binary
classification model?
A. Accuracy
B. Mean Absolute Error (MAE)
C. Precision, Recall, and F1-Score
D. R-squared
