Difference between revisions of "Feature Exploration/Learning"
| Line 12: | Line 12: | ||
* [[Recursive Feature Elimination (RFE)]] | * [[Recursive Feature Elimination (RFE)]] | ||
* [[Principal Component Analysis (PCA)]] | * [[Principal Component Analysis (PCA)]] | ||
| − | * [ | + | * [http://bookdown.org/max/FES/ Feature Engineering and Selection: A Practical Approach for Predictive Models | Max Kuhn and Kjell Johnson] |
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
* [http://github.com/jontupitza Jon Tupitza's Famous Jupyter Notebooks:] | * [http://github.com/jontupitza Jon Tupitza's Famous Jupyter Notebooks:] | ||
** [http://github.com/JonTupitza/Data-Science-On-Ramp/blob/master/01-Parametric-Tests.ipynb Parametric Tests: Tests Designed for Normally-Distributed Data] | ** [http://github.com/JonTupitza/Data-Science-On-Ramp/blob/master/01-Parametric-Tests.ipynb Parametric Tests: Tests Designed for Normally-Distributed Data] | ||
| Line 26: | Line 20: | ||
** [http://github.com/JonTupitza/Data-Science-Process/blob/master/04-EDA-Correlation-Analysis.ipynb Exploratory Data Analysis - Correlation] | ** [http://github.com/JonTupitza/Data-Science-Process/blob/master/04-EDA-Correlation-Analysis.ipynb Exploratory Data Analysis - Correlation] | ||
** [http://github.com/JonTupitza/Data-Science-Process/blob/master/05-Feature-Selection.ipynb Feature Selection Techniques] | ** [http://github.com/JonTupitza/Data-Science-Process/blob/master/05-Feature-Selection.ipynb Feature Selection Techniques] | ||
| − | + | * [[Datasets]] | |
| + | * [[Batch Norm(alization) & Standardization]] | ||
| + | * [[Data Preprocessing]] | ||
| + | * [[Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog]] | ||
| + | * [[Hyperparameter]]s | ||
| + | * [[Data Augmentation]] | ||
| + | * [[Visualization]] | ||
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. [http://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained | Martin Heller - InfoWorld] | 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. [http://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html Machine learning algorithms explained | Martin Heller - InfoWorld] | ||
Revision as of 06:04, 1 June 2020
YouTube search... ...Google search
- 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)
- Feature Engineering and Selection: A Practical Approach for Predictive Models | Max Kuhn and Kjell Johnson
- Jon Tupitza's Famous Jupyter Notebooks:
- Datasets
- Batch Norm(alization) & Standardization
- Data Preprocessing
- Master Data Management (MDM) / Feature Store / Data Lineage / Data Catalog
- Hyperparameters
- Data Augmentation
- Visualization
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
