Random forest

As explained in Graph Algorithms for Data Science

A random forest model is an ensemble of decision trees whose aggregated predictions perform classification or regression.

A random forest is used as a practical classification model for turning graph-derived representations or relationship features into predictions. section 9.2 section 10.4

A typical workflow prepares labeled data, trains the classifier, and evaluates its predictions:

• Encode categorical language labels as integers with pandas factorization. section 9.3.5
• Use node2vec embeddings as inputs for predicting the languages of new streams. section 9.2 section 9.3.5
• Split features and targets into training and test sets, then fit RandomForestClassifier on the training portion. section 9.3.5 section 10.4.2
• Predict whether a future link will form from relationship features such as network distance, common neighbors, and preferential attachment. section 10.4 section 10.4.2

Predicting streamer languages

Retrieve each stream's identifier, language, and node2vec embedding; encode languages as outputs; split the data; and train a random forest classifier to predict languages for new streams. section 9.3.5

Figure 9.6 The process of predicting the language for new streams

Use the process figure to follow the path from graph-derived embeddings through classification to language predictions for new streams. section 9.2

How Graph Algorithms for Data Science covers it

How deep it goes

Walks through label encoding, train/test splitting, fitting, and evaluation while deferring hyperparameter optimization.

Examples

Uses streamer-language prediction and binary future-link prediction with graph embeddings or relationship features.

What it assumes

Requires Jupyter, Neo4j, Python, and scikit-learn while teaching label encoding and train/test preparation within the workflow.

What you can do after

Apply it via scikit-learn to train classifiers from prepared graph features and assess predictions with classification reports and confusion matrices.

How it connects

Connects random forests with node2vec, node embeddings, link prediction, feature scaling, collinearity, logistic regression, and support vector machines.

liveBookmanning.com

Read Graph Algorithms for Data Science 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
  Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIAn ensemble of randomized decision trees whose predictions are aggregated.
• 

  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
  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 Graph Algorithms for Data Science. The section links point to the parts of the book it draws on.