KFold

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

A scikit-learn cross-validation splitter that divides data into k folds for successive training and validation splits.

KFold divides available training data into k partitions, then trains and validates k model versions. Each fold uses a different partition for out-of-sample testing, while the remaining k−1 partitions provide training data. section 4.3

Use the resulting scores to make model evaluation more practical:

• Average the k scores to estimate expected performance on unseen data. section 4.3
• Use the scores’ standard deviation to quantify uncertainty in that estimate. section 4.3
• Choose k according to available data, training cost, distribution coverage, and evaluation purpose; k=5 balances precision and computation, while k=10 favors precise evaluation. section 4.3

Five-fold evaluation

With k=5, create five training and validation arrangements. Every row of the process validates on a distinct partition, and each model trains on the other four. Average the five results and report their standard deviation. section 4.3

Figure 4.9 How data is distributed between train and validation across the folds of a five-fold cross-validation strategy

A fold diagram makes the rotating validation partition and changing training composition immediately visible. section 4.3

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

How deep it goes

Explains partitioning, repeated training, score aggregation, and k selection, but does not detail implementation beyond naming scikit-learn.

Examples

Uses a five-fold diagram and recommends k=5 for balancing evaluation precision with computation.

What it assumes

Assumes training data, model training, validation, and out-of-sample performance are understood.

What you can do after

Evaluate models by producing average cross-validation performance and standard-deviation uncertainty estimates.

How it connects

Contrasts with train/test split, leave-one-out cross-validation, and bootstrapping.

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Related concepts

• StratifiedKFoldincludes · StratifiedKFold is a scikit-learn cross-validation splitter that creates folds while preserving class proportions across the folds.
• cross-validationimplements · Cross-validation is a resampling technique that evaluates a model by repeatedly fitting it on some portions of available data and assessing it on held-out portions.

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.