Difference between revisions of "Cross-Validation"
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[http://www.google.com/search?q=Cross+Validation+machine+learning+ML ...Google search] | [http://www.google.com/search?q=Cross+Validation+machine+learning+ML ...Google search] | ||
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* [[Automated Machine Learning (AML) - AutoML]] | * [[Automated Machine Learning (AML) - AutoML]] | ||
* [[Principal Component Analysis (PCA)]] | * [[Principal Component Analysis (PCA)]] | ||
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* [[Feature Exploration/Learning]] | * [[Feature Exploration/Learning]] | ||
* [[Recursive Feature Elimination (RFE)]] | * [[Recursive Feature Elimination (RFE)]] | ||
* [http://machinelearningmastery.com/rfe-feature-selection-in-python/ Recursive Feature Elimination (RFE) for Feature Selection in Python | Jason Brownlee - Machine Learning Mastery] | * [http://machinelearningmastery.com/rfe-feature-selection-in-python/ Recursive Feature Elimination (RFE) for Feature Selection in Python | Jason Brownlee - Machine Learning Mastery] | ||
| + | * [http://www.kdnuggets.com/2018/10/notes-feature-preprocessing-what-why-how.html Notes on Feature Preprocessing: The What, the Why, and the How | Matthew Mayo - KDnuggets] | ||
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| + | 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 [http://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.html Developer Guide - Amazon AWS ML] | ||
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| + | <youtube>pcZ4YlvhSKU</youtube> | ||
| + | <youtube>7RiZFKMS3cI</youtube> | ||
| + | <youtube>MYnxxRoPiwI</youtube> | ||
| + | <youtube>xlHk4okO8Ls</youtube> | ||
Revision as of 09:21, 30 May 2020
YouTube search... ...Google search
- Automated Machine Learning (AML) - AutoML
- Principal Component Analysis (PCA)
- Feature Exploration/Learning
- Recursive Feature Elimination (RFE)
- Recursive Feature Elimination (RFE) for Feature Selection in Python | Jason Brownlee - Machine Learning Mastery
- Notes on Feature Preprocessing: The What, the Why, and the How | Matthew Mayo - KDnuggets
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 Developer Guide - Amazon AWS ML
