How deeply does Machine Learning Platform Engineering cover random forest?

Explains operational tracking around training but does not detail forest construction, randomized sampling, or tree internals.

Random forest

As explained in Machine Learning Platform Engineering

A random forest is an ensemble model that combines predictions from multiple decision trees trained with randomized sampling or feature selection.

A random forest serves as one candidate model in a tracked income-classification workflow, alongside a decision tree and XGBoost. Its training run records the dataset inputs, parameters, accuracy, AUC, and saved model so results remain reproducible and comparable. section 4.1.2

A production-oriented run connects the model to:

Reference, training, and test datasets stored in MinIO. section 4.1.2
MLflow metadata containing feature columns, types, row counts, parameters, metrics, and model artifacts. section 4.1.2
Accuracy and area-under-the-curve measurements for comparing candidate models. section 4.1.2

Tracked income classifier

Prepare feature data and train/test splits, start an MLflow run, save dataset paths to MinIO, fit the random forest, then log its parameters, accuracy, AUC, and serialized model. The resulting run exposes metrics and artifacts for inspection in the MLflow UI. section 4.1.2

How Machine Learning Platform Engineering covers it

Cover of Machine Learning Platform Engineering

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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 Platform Engineering. The section links point to the parts of the book it draws on.