Random forest

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

An ensemble of randomized decision trees whose predictions are aggregated.

A random forest combines many randomized decision trees into one prediction. Each tree sees a different sample of training cases and a changing set of candidate features at every split, producing diverse predictions that are aggregated for greater reliability. section 5.1.2 section 5.6

The main design choices create a practical bias–variance trade-off:

• Bootstrapping gives trees slightly different training samples, while feature sampling creates different tree structures and less-correlated predictions. section 5.1.2
• Binary classification uses majority voting: the most frequent tree prediction becomes the forest’s class prediction. section 5.1.2
• Useful tuning controls include the number of trees, maximum features per split, maximum tree depth, and minimum terminal-branch size. section 5.6
• More trees can improve reliability but increase computation; forests can also estimate feature importance and case similarity. section 5.6

A tabular classification workflow

A scikit-learn pipeline can one-hot encode categorical columns, pass through continuous columns, and fit a RandomForestClassifier with 300 trees, a minimum leaf size of 3, shuffled five-fold cross-validation, and a fixed random state. The same family of randomized trees can serve as a RandomForestRegressor inside IterativeImputer for multivariate missing-value estimation; deeper or larger forests improve estimates at additional computational cost. section 5.1.2 section 6.1.1

Figure 5.4 How random forests arrives at its results by combining the different data partitioning due to its decision trees thanks to majority voting

Visualizes how different tree partitions combine through majority voting into a final classification. section 5.1.2

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

How deep it goes

Explains bootstrapping, per-split feature sampling, voting, tuning, variance reduction, and computational trade-offs.

Examples

Uses binary classification, majority voting, a scikit-learn preprocessing pipeline, and RandomForestRegressor-based missing-value estimation.

What it assumes

Assumes decision trees, binary splits, bootstrapping, feature sampling, and basic ensemble strategies.

What you can do after

Apply it via scikit-learn for classification, regression-based imputation, feature-importance analysis, and case-similarity measurement.

How it connects

Connects decision trees, bagging, averaging, boosting, feature importance, extremely randomized trees, and iterative imputation.

liveBookmanning.com

Read Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Statistics Every Programmer NeedsA random forest is an ensemble model that combines predictions from multiple randomized decision trees.
• 

  liveBookmanning.com
  Ensemble Methods for Machine LearningRandomForestRegressor is a scikit-learn estimator that predicts continuous targets by aggregating randomized decision-tree regressors.
• 

  liveBookmanning.com
  Grokking Machine LearningAn ensemble model that combines predictions from many decision trees, typically trained with randomized data or feature selection.
• 

  liveBookmanning.com
  Outlier Detection in PythonRandom forest is an ensemble learning method that combines predictions from multiple decision trees built using randomized data and feature selection.
• 

  liveBookmanning.com
  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.
• 

  liveBookmanning.com
  Investing for ProgrammersA random forest is an ensemble algorithm that combines predictions from multiple decision trees.
• 

  liveBookmanning.com
  Graph Algorithms for Data ScienceA random forest model is an ensemble of decision trees whose aggregated predictions perform classification or regression.
• 

  liveBookmanning.com
  Data-Driven CybersecurityA random forest is an ensemble machine-learning model that combines predictions from multiple decision trees.
• 

  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 Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.