Difference between revisions of "Cross-Validation"
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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] | 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] | ||
| − | <youtube> | + | * Method of estimating expected prediction error |
| − | <youtube> | + | * Helps selecting the best fit model |
| − | <youtube> | + | * Help ensuring model is not over fit |
| − | <youtube> | + | |
| + | Types of Cross Validation: | ||
| + | |||
| + | * K-Fold | ||
| + | * Leave One Out | ||
| + | * Bootstrap | ||
| + | * Hold Out | ||
| + | |||
| + | <youtube>e0JcXMzhtdY</youtube> | ||
| + | <youtube>fSytzGwwBVw</youtube> | ||
| + | <youtube>sFO2ff-gTh0</youtube> | ||
| + | <youtube>TIgfjmp-4BA</youtube> | ||
| + | <youtube>6dbrR-WymjI</youtube> | ||
| + | <youtube>gJo0uNL-5Qw</youtube> | ||
| + | <youtube>7062skdX05Y</youtube> | ||
| + | <youtube>AU6OS_uq0mU</youtube> | ||
| + | <youtube>QupMZFeyETM</youtube> | ||
| + | <youtube>CBHaInynRzU</youtube> | ||
| + | <youtube>2wQlD46eICE</youtube> | ||
| + | <youtube>qOwT553oMzs</youtube> | ||
Revision as of 09:32, 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
- 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
