Difference between revisions of "Cross-Validation"
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* [[Evaluating Machine Learning Models]] | * [[Evaluating Machine Learning Models]] | ||
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Revision as of 09:34, 30 September 2020
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- Automated Learning
- Principal Component Analysis (PCA)
- Feature Exploration/Learning
- Recursive Feature Elimination (RFE)
- Cross-Validation | AWS
- Evaluating Machine Learning Models
- Monte Carlo
- Backtesting
a technique for evaluating Machine Learning (ML) models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data. Use cross-validation to detect overfitting, ie, failing to generalize a pattern Developer Guide - Amazon AWS ML
- Method of estimating expected prediction error
- Helps selecting the best fit model
- Help ensuring model is not over fit
Types of Cross Validation:
- K-Fold
- Leave One Out
- Bootstrap
- Hold Out
