Feature selection

As explained in Investing for Programmers

Feature selection is the process of choosing input variables that are most relevant or useful for a predictive model.

Feature selection chooses input variables most likely to help a supervised model predict a defined label. In financial research, candidates can include prices, expenses, earnings, macroeconomic indicators, or sector-specific data such as weather. section 8.1.2

Use candidate features carefully:

• Match specialized features to the companies they can affect; otherwise, irrelevant columns increase the signal-to-noise problem. section 8.1.2
• Construct a time-series dataset after selecting features, and choose an assessment interval that does not make signals too volatile or obsolete. section 8.1.2

Labeled stock dataset

A dataset can use stock attributes as feature columns and a future price movement as the label. Figure 8.4 represents this structure before supervised modeling. section 8.1.2

Figure 8.4 A labeled dataset for which we need to select features. Each feature will represent one column.

Inspect the labeled dataset figure to see how each candidate feature becomes a column associated with an intended outcome. section 8.1.2

How Investing for Programmers covers it

How deep it goes

Defines candidate sources and selection risks, then stops short of specifying an automated feature-ranking procedure.

Examples

Uses prices, expenses, earnings, macroeconomic indicators, weather data, and labeled stock-price datasets as examples.

What it assumes

Assumes familiarity with predictive modeling and financial variables while defining labels, features, and dataset structure through context.

What you can do after

Apply it by narrowing candidate variables, matching them to relevant companies, and constructing a labeled time-series dataset.

How it connects

Connects feature selection with supervised learning, labels, time series, regression, and signal-to-noise ratio.

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Also explained in

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  Machine Learning System DesignFeature selection is the process of choosing a subset of available input features for a machine learning model.
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  Grokking Machine LearningFeature selection is a technique that chooses relevant input features and removes unnecessary or harmful ones before model training.
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  Statistics Every Programmer NeedsFeature selection is the process of choosing a subset of input variables for use in a predictive model.
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  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIFeature sampling selects a subset of input features as candidates for modeling or a split.
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  Deep Learning with R, Third EditionFeature selection is the process of choosing a subset of input features that are useful for a machine-learning task.
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  Outlier Detection in PythonFeature selection is the process of choosing a subset of input features that are most useful for a specified analysis or prediction task.
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  Evolutionary Deep Learning: Genetic algorithms and neural networksFeature selection is the process of identifying and retaining input variables that contribute to a model's predictions while excluding less-relevant variables.

Compare how 8 books explain feature selection →

Related concepts

• feature engineeringpart of · Feature engineering is the creation, transformation, or selection of input features from data to provide useful representations for analysis or modeling.
• inputuses · An input is a value, information, or signal supplied to a system, computation, or interface for processing or to influence its behavior.
• curse of dimensionalityaddresses · The curse of dimensionality is the set of statistical, geometric, and computational difficulties that tend to increase as the number of dimensions grows.
• L1 regularizationused by · L1 regularization is a technique that adds a penalty proportional to the sum of absolute model-parameter values, encouraging sparse parameter estimates.
• random forestused by · A random forest is an ensemble model that combines predictions from multiple decision trees built with randomized data samples, feature selections, or both.

This explainer was generated by Manning's knowledge graph from the text of Investing for Programmers. The section links point to the parts of the book it draws on.