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Question & Answer1
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Explore TopicChoose the Best Answer
A
L1 Regularization (Lasso)
B
L2 Regularization (Ridge)
C
Dropout Regularization
D
No regularization at all
Understanding the Answer
Let's break down why this is correct
Answer
The data scientist should use L2 regularization, also called ridge regression. L2 adds a penalty proportional to the square of the model coefficients, which shrinks all weights toward zero but keeps them non‑zero, preserving the overall pattern learned. This reduces variance by discouraging overly large weights that would fit noise, while still allowing the model to capture the underlying signal. For example, if a linear model learns a coefficient of 10 on a noisy feature, ridge would reduce it to about 8, keeping the feature useful but less extreme. Thus, L2 regularization strikes a good balance between preventing overfitting and maintaining predictive power.
Detailed Explanation
It adds a small penalty to each coefficient, keeping them close to zero. Other options are incorrect because Many think it always stops overfitting, but it can force some variables to disappear entirely; Dropout is meant for neural networks, where it randomly ignores neurons during training.
Key Concepts
Regularization techniques
Overfitting in predictive models
Model complexity
Topic
Regularizers in Predictive Models
Difficulty
medium level question
Cognitive Level
understand
Practice Similar Questions
Test your understanding with related questions
1
Question 1In the context of parametrized predictors, which combination of estimation techniques and regularization methods can lead to improved model evaluation by reducing overfitting?
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2
Question 2Which type of loss function incorporates regularization to prevent overfitting in a machine learning model?
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3
Question 3In 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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4
Question 4A data scientist is working on a predictive model to forecast housing prices. They notice that the model tends to overfit the training data, leading to poor performance on unseen data. To address this issue, they decide to implement regularization. Which of the following approaches would best help them reduce overfitting while maintaining model interpretability?
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5
Question 5Arrange 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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6
Question 6How does the implementation of regularization techniques in deep learning models help mitigate overfitting, and what impact does this have on decision-making processes in business applications?
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7
Question 7In the context of multi-class loss functions, how do precision and recall impact the choice of regularization techniques to prevent overfitting?
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