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Authored by THENNARASU. S

Special Education

2nd Grade

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61 questions

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1.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

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

2.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

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

3.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

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

Wrapper Methods
Embedded Methods
Random Forest Importance
Filter Methods

4.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

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

5.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

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

6.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

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.

7.

MULTIPLE CHOICE QUESTION

1 min • 1 pt

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

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