Tree-based method
A tree-based method is a predictive or decision-making technique that represents its rules or decision structure using one or more trees.
Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI
A tree-based model is a predictive model whose decision structure is formed from one or more decision trees.
- How it is used
- Handles tabular prediction with relatively little preprocessing, supporting both classification and regression workflows.
- How deep it goes
- Explains roots, branches, leaves, split search, impurity criteria, and pruning, but does not detail ensemble variants.
- Examples
- Uses animal classification by legs and eyes and Airbnb NYC listing classification with categorical and numeric features.
- What you can do after
- Apply it via scikit-learn pipelines, preprocessing features and evaluating a DecisionTreeClassifier with cross-validation.
Machine Learning Algorithms in Depth
A tree-based model makes predictions by recursively partitioning an input space into regions represented by nodes and leaves.
- How it is used
- Uses recursive partitioning for interpretable classification and regression, with feature importance as an additional source of insight.
- How deep it goes
- Derives feature-threshold search and Gini impurity, mentions entropy, and stops short of detailing the full objective equations.
- Examples
- Demonstrates Python classes for CART and applies the workflow to the Iris dataset with train-test splitting and accuracy scoring.
- What you can do after
- Build a basic CART classifier from scratch with node state, split search, depth control, and accuracy evaluation.
Read how Machine Learning Algorithms in Depth explains tree-based method →
Demand Forecasting Best Practices
A tree-based machine learning model makes predictions using one or more decision-tree structures that represent relationships between inputs and outputs.
- How it is used
- Uses tree-based models to forecast demand and inspect which data features receive the model’s attention.
- How deep it goes
- Explains feature importance and scenario analysis, but stops short of exposing level, trend, seasonality, or direct causal effects.
- Examples
- Uses feature rankings, weather as a low-importance driver, and promotion scenarios showing uplift and self-cannibalization.
- What you can do after
- Evaluate demand drivers and compare forecast scenarios while avoiding causal claims from feature importance alone.
Read how Demand Forecasting Best Practices explains tree-based method →
Related concepts
- random forestincludes · A random forest is an ensemble model that combines predictions from multiple decision trees built with randomized data samples, feature selections, or both.
- importance scoreused by · An importance score is a numerical value indicating an item's relative significance, relevance, or contribution within a specified context.
- XGBoostincludes · XGBoost is an open-source software library for efficient gradient-boosted decision-tree algorithms used in supervised machine learning.
- gradient boostingincludes · Gradient boosting is an ensemble-learning technique that sequentially adds predictive models, typically weak learners, to reduce a loss by fitting its gradient.
- decision treeincludes · A decision tree is a branching structure that represents sequential tests or choices, with paths leading to outcomes, predictions, or actions.
- label encodingused by · Label encoding is a categorical-data transformation that maps each category to a numerical code, which may imply an ordering among categories.
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