Difference between revisions of "Feature Exploration/Learning"
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* [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] | * [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] | ||
* [[Automated Machine Learning (AML) - AutoML]] | * [[Automated Machine Learning (AML) - AutoML]] | ||
| + | * [[Recursive Feature Elimination (RFE)]] | ||
* [[Principal Component Analysis (PCA)]] | * [[Principal Component Analysis (PCA)]] | ||
* [[Datasets]] | * [[Datasets]] | ||
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* [[Data Augmentation]] | * [[Data Augmentation]] | ||
* [[Visualization]] | * [[Visualization]] | ||
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* [[Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog]] | * [[Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog]] | ||
* [http://github.com/jontupitza Jon Tupitza's Famous Jupyter Notebooks:] | * [http://github.com/jontupitza Jon Tupitza's Famous Jupyter Notebooks:] | ||
Revision as of 22:24, 29 May 2020
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- Notes on Feature Preprocessing: The What, the Why, and the How | Matthew Mayo - KDnuggets
- Automated Machine Learning (AML) - AutoML
- Recursive Feature Elimination (RFE)
- Principal Component Analysis (PCA)
- Datasets
- Batch Norm(alization) & Standardization
- Data Preprocessing
- Hyperparameters
- Data Augmentation
- Visualization
- Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog
- Jon Tupitza's Famous Jupyter Notebooks:
A feature is an individual measurable property or characteristic of a phenomenon being observed. The concept of a “feature” is related to that of an explanatory variable, which is used in statistical techniques such as linear regression. Feature vectors combine all of the features for a single row into a numerical vector. Part of the art of choosing features is to pick a minimum set of independent variables that explain the problem. If two variables are highly correlated, either they need to be combined into a single feature, or one should be dropped. Sometimes people perform principal component analysis to convert correlated variables into a set of linearly uncorrelated variables. Some of the transformations that people use to construct new features or reduce the dimensionality of feature vectors are simple. For example, subtract Year of Birth from Year of Death and you construct Age at Death, which is a prime independent variable for lifetime and mortality analysis. In other cases, feature construction may not be so obvious. Machine learning algorithms explained | Martin Heller - InfoWorld
Sparse Coding - Feature extraction
