In credit scoring, cross-validation is part of evaluating a baseline XGBoost model trained on WoE-coded features. Model performance is reviewed alongside confusion matrices before probabilities are mapped to credit scores. section 5.2.3
Cross-validation
Cross-validation is a model evaluation technique that partitions data into subsets and repeatedly trains and evaluates a model on different subsets.
How Financial AI in Practice covers it
- How deep it goes
- Mentions cross-validated evaluation but does not specify fold setup or evaluation procedure.
- Examples
- Pairs cross-validated performance review with confusion matrices for a WoE-based credit model.
- What you can do after
- Evaluate credit-model performance as part of a credit-scoring workflow.
- How it connects
- Connects cross-validation with WoE feature encoding, XGBoost, and confusion matrices.
Also explained in
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Ensemble Methods for Machine LearningK-fold cross-validation partitions data into k folds and repeatedly trains and evaluates a model using one fold for validation and the others for training.
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Causal Inference for Data ScienceCross-validation is a model-evaluation procedure that repeatedly partitions data into training and validation portions to estimate predictive performance.
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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AICross-validation is a model-evaluation technique that repeatedly partitions data into training and validation subsets.
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Effective Conversational AIK-fold cross-validation is a model-evaluation technique that partitions a dataset into k folds and repeatedly evaluates a model with each fold held out in turn.
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Deep Learning with R, Third EditionK-fold cross-validation is an evaluation procedure that partitions data into K subsets and repeatedly trains on K−1 subsets while evaluating on the remaining subset.
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Deep Learning with Python, Third EditionA validation procedure that partitions data into K folds, trains on K−1 folds, and evaluates on the remaining fold in rotation.
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Grokking StatisticsCross-validation repeatedly partitions data into complementary training and evaluation folds to assess model performance.
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Delivering Machine Learning Projects: From design to deploymentCross-validation is a resampling technique that repeatedly trains and evaluates a model on different partitions of available data.
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Causal AICross-validation is a model evaluation technique that assesses predictive performance on data withheld from training.
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Building LLM Applications with DSPyCross-validation is an evaluation technique that repeatedly partitions data into training and validation subsets to produce a more reliable estimate.
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Grokking Machine LearningA resampling technique that partitions a dataset into k portions and repeatedly trains on k−1 portions while validating on the remaining portion, then averages the validation scores.
Compare how 12 books explain cross-validation →
Related concepts
- covariateuses · A covariate is an observed or supplied variable included in a statistical, causal, or predictive model to describe, explain, predict, or adjust for variation in an outcome.
- training datasetuses · A training dataset is a collection of examples or data used by a learning procedure to fit or adapt a model.
- grid searchused by · Grid search is a search method that evaluates configurations formed from specified candidate values, typically every combination in their Cartesian product.
This explainer was generated by Manning's knowledge graph from the text of Financial AI in Practice. The section links point to the parts of the book it draws on.