Filter method

As explained in Machine Learning System Design

A statistical feature-selection method that evaluates each feature independently using a single-feature property or score.

A filter method selects features independently from the model by ranking single-feature statistical properties or relationships among features. It provides a fast first pass before modeling or more expensive selection. section 11.3.3

Use filter methods in two main ways:

• Rank individual features by variance, granularity, consistency, or correlation with the target, then retain the top-N subset. section 11.3.3
• Compare features with rank correlation or mutual information to identify redundant pairs and remove one without expected performance loss. section 11.3.3
• Scale the method to high-dimensional data and use it before fitting a model or applying wrapper-based selection. section 11.3.3

Pre-training feature review

An automatic report can flag a high feature-interaction score as a warning that reducing one of two similar features may hurt model performance before training begins. section 11.3.3

Figure 11.8 Families of feature selection methods

Compare filter methods with wrapper and embedded feature-selection families. section 11.3.3

How Machine Learning System Design covers it

How deep it goes

Explains univariate and multivariate ranking, redundancy checks, and top-N selection, but stops short of algorithmic implementation details.

Examples

Uses top-N ranking, rank correlation, mutual information, and an automatic report warning about feature interaction.

What it assumes

Assumes familiarity with features and models while defining univariate and multivariate selection from their statistical properties.

What you can do after

Apply it to rank features, retain a top-N subset, and flag redundant features before training.

How it connects

Connects feature selection with wrapper methods, embedded methods, feature redundancy, correlation, and mutual information.

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Related concepts

• feature selectionkind of · Feature selection is the process of choosing a subset of input features for use in a modeling, analysis, or prediction task.
• dimensionality reductionaddresses · Dimensionality reduction is the transformation of data into a representation with fewer dimensions while retaining selected information, structure, or relationships.
• filteringkind of · Filtering is the operation of selecting or excluding items from data or a collection according to specified criteria.
• embedded methodcontrasts with · A feature-selection technique that derives feature choices from the parameters or importance values of a model trained as part of the selection process.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning System Design. The section links point to the parts of the book it draws on.