Difference between revisions of "Cross-Validation"
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|description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | ||
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| − | [ | + | [https://www.youtube.com/results?search_query=Cross+Validation+machine+learning+ML YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=Cross+Validation+machine+learning+ML ...Google search] |
| − | * [[Automated | + | * [[Algorithm Administration#Automated Learning|Automated Learning]] |
* [[Principal Component Analysis (PCA)]] | * [[Principal Component Analysis (PCA)]] | ||
* [[Feature Exploration/Learning]] | * [[Feature Exploration/Learning]] | ||
* [[Recursive Feature Elimination (RFE)]] | * [[Recursive Feature Elimination (RFE)]] | ||
| − | * [ | + | * [https://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Cross-Validation | AWS] |
| − | * [ | + | * [[Evaluating Machine Learning Models]] |
| + | * [[Monte Carlo]] | ||
| + | * [[Backtesting]] | ||
| − | a technique for evaluating 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 [ | + | |
| + | 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 [https://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Developer Guide - Amazon AWS ML] | ||
* Method of estimating expected prediction error | * Method of estimating expected prediction error | ||
Latest revision as of 07:54, 28 March 2023
YouTube search... ...Google search
- 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
