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Quiz 4: Advanced Regression Concepts
Test your knowledge on Logistic Regression, Regularization, and Over/Underfitting concepts.
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1. What is the primary use of Logistic Regression?
Predicting continuous values
Binary classification
Clustering data points
Reducing dimensionality
2. Which function does Logistic Regression use to map inputs to probabilities?
Linear function
Sigmoid function
Hyperbolic tangent
ReLU function
3. What is the typical threshold for classification in Logistic Regression?
0.25
0.5
0.75
1.0
4. What does regularization aim to prevent in machine learning models?
Underfitting
Overfitting
Increasing variance
Reducing model complexity
5. Which regularization technique drives some weights to exactly zero?
L1 Regularization (Lasso)
L2 Regularization (Ridge)
ElasticNet
Dropout
6. Which regularization technique shrinks weights but does not eliminate them?
L1 Regularization (Lasso)
L2 Regularization (Ridge)
ElasticNet
Batch Normalization
7. What is the effect of a high regularization strength (λ)?
Increases model complexity
Simplifies the model
Has no effect on the model
Leads to overfitting
8. What is overfitting in a machine learning model?
When the model performs poorly on training data
When the model captures noise in the training data
When the model underestimates the patterns in the data
When the model generalizes well to unseen data
9. What characterizes underfitting in a model?
High training error and low test error
Low training error and high test error
High bias and low variance
Low bias and high variance
10. What is the optimal condition for model fitting?
When the model has minimal bias and high variance
When the model has minimal bias and variance
When the model overfits the data
When the model underfits the data
11. What role does the bias term play in Logistic Regression?
It determines the slope of the decision boundary
It shifts the decision boundary
It controls the learning rate
It regularizes the model
12. Which graph shape represents L1 regularization penalties?
U-shaped
V-shaped
Linear
Exponential
13. Which graph shape represents L2 regularization penalties?
U-shaped
V-shaped
Linear
Exponential
14. What happens to test error when a model overfits?
Test error decreases
Test error increases
Test error remains unchanged
Test error becomes zero
15. Which parameter is adjusted to control regularization strength?
Learning rate
Regularization strength (λ)
Bias
Batch size
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