Random forest

As explained in Investing for Programmers

A random forest is an ensemble algorithm that combines predictions from multiple decision trees.

How Investing for Programmers covers it

How deep it goes

Demonstrates data preparation, chronological splitting, fitting, prediction, RMSE validation, and plotting, but not tree internals.

Examples

Uses AAPL historical prices, next-day closing-price labels, RMSE, and actual-versus-forecast plots.

What it assumes

Assumes basic labeled-data and supervised-learning ideas while demonstrating feature-label construction and train-test validation.

What you can do after

Build it from scratch with Python data libraries and evaluate predictions against held-out historical prices.

How it connects

Connects supervised learning with overfitting, feature selection, time-series prediction, and ARIMA.

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Also explained in

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  Statistics Every Programmer NeedsA random forest is an ensemble model that combines predictions from multiple randomized decision trees.
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  Ensemble Methods for Machine LearningRandomForestRegressor is a scikit-learn estimator that predicts continuous targets by aggregating randomized decision-tree regressors.
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  Grokking Machine LearningAn ensemble model that combines predictions from many decision trees, typically trained with randomized data or feature selection.
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  liveBookmanning.com
  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIAn ensemble of randomized decision trees whose predictions are aggregated.
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  Outlier Detection in PythonRandom forest is an ensemble learning method that combines predictions from multiple decision trees built using randomized data and feature selection.
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  Cyber Threat HuntingA random forest is an ensemble machine-learning algorithm that combines predictions from multiple decision trees trained with randomized sampling or feature selection.
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  Graph Algorithms for Data ScienceA random forest model is an ensemble of decision trees whose aggregated predictions perform classification or regression.
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  Data-Driven CybersecurityA random forest is an ensemble machine-learning model that combines predictions from multiple decision trees.
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  liveBookmanning.com
  Machine Learning Platform EngineeringA random forest is an ensemble model that combines predictions from multiple decision trees trained with randomized sampling or feature selection.
• 

  liveBookmanning.com
  Financial AI in PracticeA random forest is an ensemble model that combines predictions from multiple decision trees trained with randomized data and feature selection.

Compare how 11 books explain random forest →

Related concepts

• decision treehas part · A decision tree is a branching structure that represents sequential tests or choices, with paths leading to outcomes, predictions, or actions.
• ensemblekind of · An ensemble is a technique that combines or considers outputs from multiple models or component methods collectively to produce or assess a joint result.
• bagginguses · Bagging, or bootstrap aggregation, is an ensemble technique that trains multiple models on bootstrap samples of data and combines their predictions.
• importance scoreuses · An importance score is a numerical value indicating an item's relative significance, relevance, or contribution within a specified context.
• bootstrap samplingused by · Bootstrap sampling is a resampling technique that repeatedly draws observations with replacement from observed data to estimate a statistic’s sampling behavior or variability.

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.