Filter methods for feature selection are used (select one best answer):

Question

BlackTom AI · Accepted Answer

The question asks about when filter methods for feature selection are used in the modeling workflow.
Option 1: 'Before applying a classification algorithm' — This aligns with the typical practice in feature selection, where filter methods assess features independently of any learning algorithm to reduce dimensionality prior to model training.
Option 2: 'As part of the classification algorithm' — Filter methods operate outside the classifier; if integrated directly inside a classifier, that would resemble embedded or wrapper methods, not a pure filter approach.
Option 3: 'After applying a classification algorithm' — Using features after training to inform something would be post-hoc analysis, not a standard pre-processing step for feature selection; filters are meant to influence the features used for training, not the outputs after training.
Option 4: 'At any point of model training' — This suggests flexibility to apply filters throughout training, which contradicts the typical separation of feature ranking (filter) from model fitting; it is generally performed before model fitting, not arbitrarily during training.
In summary, filter methods are designed to rank and select features prior to the classifier being trained, thereby simplifying the model input and potentially improving performance without relying on the classifier itself.

Filter methods for feature selection are used (select one best answer):Single choice

Similar Questions

Feature selection differs from feature extraction in that feature selection occurs when latent features are automatically and algorithmically learned from data.

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

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