Learning Path
Question & Answer1
Understand Question2
Review Options3
Learn Explanation4
Explore TopicChoose the Best Answer
A
They help reduce overfitting by adding penalties to large coefficients.
B
They increase the model's complexity by allowing more parameters.
C
They make the model less sensitive to training data without affecting test data.
D
They are only applicable to linear models.
Understanding the Answer
Let's break down why this is correct
Answer
Adding an L1 or L2 penalty to the loss function makes the model prefer smaller, simpler weights, which helps it generalize better to new data. L1 regularization pushes many coefficients exactly to zero, effectively selecting a subset of features and making the model easier to interpret. L2 regularization shrinks all weights toward zero without eliminating them, which reduces variance by preventing any single feature from dominating the prediction. Together they keep the model flexible enough to fit the training data while avoiding over‑fitting. For example, when predicting house prices, L1 might drop irrelevant variables like “pet name,” while L2 keeps the remaining coefficients small, producing a smoother, more reliable forecast.
Detailed Explanation
Regularization adds a small cost to large coefficient values, which keeps the model from fitting every tiny detail in the training data. Other options are incorrect because Some think regularization makes the model more complex, but it actually does the opposite; Regularization does not leave test data untouched; it improves how the model behaves on new data.
Key Concepts
Regularization in predictive modeling
Overfitting in machine learning
Parameter tuning in models
Topic
Regularizers in Predictive Models
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
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Question 1What is the primary purpose of using regularizers in predictive models?
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Question 2How do penalty terms in regularization techniques assist in feature selection within predictive models?
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Question 3How does the introduction of a penalty term in regularization affect model complexity and the process of hyperparameter tuning in predictive models?
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Question 4In the context of predictive modeling, how does the introduction of a penalty term through regularization techniques influence predictive accuracy, particularly in high-dimensional datasets?
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Question 5Which of the following statements about regularizers in predictive models are correct? Select all that apply.
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Question 6In what way do regularizers like Lasso and Ridge improve predictive models?
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Question 7What effect does increasing the strength of Lasso regularization (`1) have on a predictive model's coefficients?
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Question 8Arrange the following steps in the correct order for applying regularization in predictive modeling: A) Analyze the model's performance on training data, B) Choose a regularization technique, C) Evaluate the model on validation data, D) Train the model with regularization applied.
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Question 9How do regularization techniques influence model performance when implementing identity mapping in deep models?
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