Feature selection for machine learning in Python
Several techniques to select features in a machine learning problem
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What You Will Learn!
- Feature selection for regression problems
- Feature selection for classification problems
- Recursive Feature Elimination
- Recursive Feature Elimination with Cross-Validation
Description
In this practical course, we are going to focus on the feature selection approaches for machine learning using Python programming language.
Selecting the best set of features is crucial for the success of a machine learning project. Too many features will not make the model learn the information properly while using a few features won't carry enough information.
Each model has its own needs regarding the features to learn from, so it's important to select them properly.
If you want a stable and efficient model, selecting the right number of variables is one of the most important steps in your data science pipeline.
With this course, you are going to learn:
Feature selection for regression models
Feature selection for classification models
Recursive Feature Elimination
Recursive Feature Elimination with cross-validation
All the lessons of this course start with a brief introduction and end with a practical example in Python programming language and its powerful scikit-learn library. The environment that will be used is Jupyter, which is a standard in the data science industry. All the Jupyter notebooks are downloadable.
This course is part of my Supervised Machine Learning in Python online course, so you'll find some lessons that are already included in the more extensive course.
Who Should Attend!
- Aspiring data scientists
