How deeply does Grokking Machine Learning cover DecisionTreeRegressor?

Explains cutoff testing, mean squared error, recursive splitting, depth control, and leaf predictions.

DecisionTreeRegressor

As explained in Grokking Machine Learning

A scikit-learn estimator class that fits regression decision trees to numerical targets.

DecisionTreeRegressor predicts numerical targets by repeatedly asking cutoff questions. Each split divides the data, assigns each side its average target, and chooses the cutoff with the lowest mean squared error. section 9.6

Predicting app engagement from age

For eight users, age predicts weekly app-opening days. Testing possible age cutoffs selects 35 because it produces the smallest MSE. Setting max_depth=2 creates splits at 35, 15, and 65, with leaf predictions of 7, 6, 1.33, and 4.5. section 9.6

Table 9.6 A small dataset with eight users, their age, and their engagement with our app. The engagement is measured in the number of days when they opened the app in one week. (view table figure)

Age	Engagement
10	7
20	5
30	7
40	1
50	2
60	1
70	5
80	4

Inspect the eight-user age and engagement dataset used to build the regression tree. section 9.6

How Grokking Machine Learning covers it

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Also explained in

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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA scikit-learn estimator that fits a decision tree for predicting numeric target values.

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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.

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