Gini impurity

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover Gini impurity?

Defines the misclassification probability and demonstrates threshold scoring, but stops short of a mathematical derivation.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

Gini impurity is the probability of misclassifying an element labeled according to the class distribution of its subset.

Gini impurity measures how likely a randomly selected item would be misclassified if it received a label drawn from its subset’s class distribution. A pure subset has one class; a mixed subset has greater impurity. section 5.1

A classification tree uses Gini impurity to compare candidate feature splits. It prefers splits whose resulting subsets are more homogeneous, typically combining the child impurities with their subset sizes. section 5.1

Choosing a split efficiently

For an Airbnb price-range classification task, latitude offers many possible thresholds. Testing every unique latitude took about 46.9 seconds; evaluating 256 histogram-bin thresholds produced the same selected split and score in about 562 milliseconds. section 5.4.3

How Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI covers it

Cover of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

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Also explained in

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Grokking Machine LearningA decision-tree impurity measure that quantifies the heterogeneity of class labels within a node.
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Statistics Every Programmer NeedsGini impurity is a node-impurity measure equal to the probability of misclassifying an observation when labels are assigned according to the node's class distribution.
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Grokking AI Algorithms, Second EditionThe Gini index is an impurity measure that quantifies the likelihood of incorrect class assignment in a labeled dataset.
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Machine Learning Algorithms in DepthThe Gini index is an impurity measure for evaluating the heterogeneity of class labels in a set or node.

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Related concepts

entropyalternative to · Entropy is a quantitative measure of uncertainty or dispersion in a probability distribution and, more generally, of disorder in the possible states of a system.
CARTused by · CART is a decision-tree learning algorithm that recursively partitions data into binary branches for classification or regression.
decision nodemeasures · A decision node is a tree node where a decision, test, or action selection determines which successor branch is followed.
fairnessmeasures · Fairness is the principle or property of allocating treatment, opportunities, or outcomes equitably among relevant people, groups, or participants according to appropriate criteria.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.