Split

As explained in Statistics Slam Dunk

A tree split is a rule that partitions observations into child regions according to the value of a predictor.

How Statistics Slam Dunk covers it

How deep it goes

Explains regional partitioning, split importance, thresholds, and terminal nodes, but not the algorithm selecting each split.

Examples

Uses screen_assists_pts greater than 26.05 and a visualized ten-terminal-node wins tree.

What it assumes

Assumes predictor variables and a training-data split, while relating tree syntax to multiple regression.

What you can do after

Evaluate/understand how predictor thresholds divide observations and lead to regional predictions.

How it connects

Connects splits with regression trees, predictors, terminal nodes, bagging, random forests, and boosting.

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Also explained in

• 

  liveBookmanning.com
  Statistics Every Programmer NeedsA split is a decision rule that partitions observations at a decision-tree node according to a feature and condition.

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Related concepts

• decision treepart of · A decision tree is a branching structure that represents sequential tests or choices, with paths leading to outcomes, predictions, or actions.
• accuracymeasured by · Accuracy is the degree to which an output, prediction, or measurement agrees with a correct or reference outcome under a specified criterion.
• thresholduses · A threshold is a boundary value against which a quantity is compared to determine whether a condition is met or an action or decision is triggered.

This explainer was generated by Manning's knowledge graph from the text of Statistics Slam Dunk. The section links point to the parts of the book it draws on.