Feature selection increases the risk of overfitting

Question

BlackTom AI · Accepted Answer

Statement to evaluate: 'Feature selection increases the risk of overfitting'.
Option 1: True. This answer would imply that performing feature selection inherently raises overfitting risk in all circumstances. In practice, this is not universally true. While poor feature selection strategies or leakage (using test data to select features) can indeed lead to overfitting, feature selection can also reduce overfitting by removing irrelevant or noisy features and simplifying the model. Therefore, labeling it as universally increasing overfitting is misleading and context-dependent.
Option 2: False. This is the more accurate stance as a blanket claim. Properly applied feature selection can help prevent overfitting by reducing the dimensionality of the data, which lowers variance and simplifies the model. However, it should be performed within a proper cross-validation framework to avoid data leakage; if done incorrectly, it can still cause overfitting. The core idea is that the statement is not universally true in all scenarios, so the safest classification is that the statement is false when considering correct, leakage-free feature selection practices.
In summary, while feature selection can contribute to overfitting if mishandled, it is not inherently increasing the risk in every case, which is why the blanket statement is deemed false. The nuanced view is that the impact of feature selection on overfitting depends on methodology and safeguards against data leakage.

Feature selection increases the risk of overfitting

View Explanation

Log in for full answers

Similar Questions

Refer to the feature importance graph below. If you want to simplify the model with minimal loss in predictive power, which features could you REMOVE so that the remaining features still account for at least 80% of the total importance? [2 marks]

If a feature has the same value for all records, then:

Question at position 26 True/False Question: Forward selection is faster than backward selection if few features are relevant to prediction. TrueFalse

Question at position 10 Select all correct answers: Which of the following are common methods of feature selection? kNNPrincipal Component Analysis (PCA)Ridge RegressionMin Max ScalingBackward EliminationLasso RegressionForward Selection

Feature selection increases the risk of overfitting

For doing Feature Selection, there is a Filtering approach and a Wrapper approach. Highlight what is true.

Term j has good discriminating properties and therefore can be a good feature if it creates an imbalanced distribution of class labels for documents that contain term j:

More Practical Tools for Students Powered by AI Study Helper

Homework AI Solver

Stylized AI Paper Writer

Plagiarism Checker Assistant

Citation AI Academic Writing Tool

In-Class Translation Assistant

AI Note Generator

AI Quiz Answers

Past Exam Questions from University Test Bank

Smart Practice Assistant

Adaptive Practice

Making Your Study Simpler