B.
To improve model performance by reducing overfitting
C.
To create new features from existing ones
D.
To visualize the data
Solution
The main goal of feature selection is to improve model performance by reducing overfitting and enhancing the model's ability to generalize to unseen data.
Correct Answer:
B
— To improve model performance by reducing overfitting
Q. What is the purpose of using cross-validation in model selection?
A.
To increase the size of the training dataset
B.
To assess how the results of a statistical analysis will generalize to an independent dataset
C.
To reduce the dimensionality of the dataset
D.
To improve the interpretability of the model
Solution
Cross-validation is used to evaluate the generalization ability of a model by partitioning the data into subsets, training on some and validating on others.
Correct Answer:
B
— To assess how the results of a statistical analysis will generalize to an independent dataset
Q. Which algorithm is commonly used for classification tasks?
A.
Linear Regression
B.
K-Nearest Neighbors
C.
Principal Component Analysis
D.
K-Means Clustering
Solution
K-Nearest Neighbors (KNN) is a popular algorithm used for classification tasks, where it classifies data points based on the classes of their nearest neighbors.
Q. Which of the following is NOT a common technique for feature scaling?
A.
Min-Max Scaling
B.
Standardization
C.
Log Transformation
D.
Feature Selection
Solution
Feature selection is the process of selecting a subset of relevant features, while Min-Max Scaling, Standardization, and Log Transformation are techniques for feature scaling.
