DecisionTreeRegressor
DecisionTreeRegressor is a scikit-learn estimator class that fits decision-tree models to predict numerical target values.
Grokking Machine Learning
A scikit-learn estimator class that fits regression decision trees to numerical targets.
- How it is used
- Fits regression trees that predict numerical labels by recursively partitioning feature values.
- How deep it goes
- Explains cutoff testing, mean squared error, recursive splitting, depth control, and leaf predictions.
- Examples
- Uses eight users’ ages and weekly app engagement, with cutoffs at 15, 35, and 65.
- What you can do after
- Apply it via scikit-learn by fitting DecisionTreeRegressor(max_depth=2) to feature and label arrays.
Read how Grokking Machine Learning explains DecisionTreeRegressor →
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
A scikit-learn estimator that fits a decision tree for predicting numeric target values.
- How it is used
- Deploys regression trees as sequential correction models inside gradient boosting, including boosting systems for classification.
- How deep it goes
- Explains the estimator’s boosting role and key complexity parameters, but does not detail tree-splitting internals.
- Examples
- Demonstrates the estimator in a custom GradientBoosting class configured with max_depth, min_samples_leaf, and random_state.
- What you can do after
- Builds a custom gradient boosting classifier using scikit-learn’s DecisionTreeRegressor and tunes basic tree-complexity controls.
Related concepts
- decision treeimplements · A decision tree is a branching structure that represents sequential tests or choices, with paths leading to outcomes, predictions, or actions.
- gradient boostingused by · Gradient boosting is an ensemble-learning technique that sequentially adds predictive models, typically weak learners, to reduce a loss by fitting its gradient.
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