Balanced classification problem

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover balanced classification problem?

Defines balance through observed class counts and contrasts it with skewed targets, without discussing resampling or algorithm internals.

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

A balanced target is a classification target whose classes contain approximately equal numbers of observations.

A balanced classification problem has classes with approximately equal numbers of observations. Checking class counts before modeling helps determine whether accuracy gives a useful performance picture. section 4.2.1

Price classification targets

A median-price target produces 24,472 cases below or at the threshold and 24,423 above it, making the binary target balanced. A five-class percentile target is also broadly balanced, with class counts ranging from 8,384 to 10,809. section 4.2.1

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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Related concepts

accuracymeasured by · Accuracy is the degree to which an output, prediction, or measurement agrees with a correct or reference outcome under a specified criterion.
area under the curveused by · The area under a curve is the integral of its values over a specified domain and, in classification evaluation, commonly summarizes performance across decision thresholds.
classifierkind of · A classifier is a predictive computational model that assigns inputs to one or more discrete classes or categories.
class imbalancecontrasts with · Class imbalance is a condition in a classification dataset where classes have substantially unequal numbers of examples.

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.