thadaaladi quiz

Which technique is primarily used for dimensionality reduction by transforming correlated features into a set of linearly uncorrelated features with orthogonal transformations?

• Backward Feature Elimination
• Forward Feature Selection
• Principal Component Analysis (PCA)
• Random Forest

What is the primary goal of the Low Variance Filter technique in dimensionality reduction?

• To remove variables with high variance
• To reduce multicollinearity
• to drop features with low variance
• To enhance data visualization

Which type of feature selection technique considers feature interaction and has low computational cost?

• Wrapper Methods
• Embedded Methods
• Random Forest Importance
• Filter Methods

Which feature selection method evaluates each feature set as brute-force by trying all possible combinations of features?

• Exhaustive feature selection
• Recursive Feature Elimination
• Backward elimination
• Forward selection

What is the primary objective of shrinkage methods like Ridge Regression and Lasso Regression in machine learning?

• To increase the size of the coefficients for better model interpretability
• To reduce the variance of the model by controlling the size of coefficients
• To make the model more complex
• To eliminate outliers from the dataset

Which of the following is a characteristic of Lasso Regression compared to Ridge Regression?

• Lasso uses the l2 norm, while Ridge uses the l1 norm.
• Lasso prefers to reduce larger coefficients and is more likely to drive coefficients to zero.
• Ridge Regression is a sparse method.
• Elastic Net combines the advantages of Lasso and Ridge.

What is the primary goal of Principal Component Analysis (PCA) in machine learning?

• To increase the dimensionality of the dataset
• To maximize the variance of data in a lower-dimensional space
• To add redundancy to the features
• To make data more complex

Which mathematical concepts are fundamental to PCA?

• Mean and Standard Deviation
• Variance and Covariance
• Regression and Classification
• Precision and Recall

What is the key objective of Linear Discriminant Analysis (LDA) in machine learning?

• To maximize variance within classes
• To minimize the number of features in the dataset
• To reduce data compression
• To maximize separation between class

What is the primary drawback of Linear Discriminant Analysis (LDA)?

• LDA performs poorly in binary classification problems.
• LDA fails when the mean of distributions is shared between classes.
• LDA cannot handle multiple features.
• LDA is computationally expensive.