Difference between revisions of "Cross-Validation"
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* [[Recursive Feature Elimination (RFE)]] | * [[Recursive Feature Elimination (RFE)]] | ||
* [http://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Cross-Validation | AWS] | * [http://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Cross-Validation | AWS] | ||
| + | * [[Evaluating Machine Learning Models]] | ||
Revision as of 21:59, 1 June 2020
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- Automated Machine Learning (AML) - AutoML
- Principal Component Analysis (PCA)
- Feature Exploration/Learning
- Recursive Feature Elimination (RFE)
- Cross-Validation | AWS
- Evaluating Machine Learning Models
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
