23+ Machine Learning Features Selection Images. Feature selection, also known as variable selection, is a powerful idea, with major implications for your machine learning workflow. The techniques for feature selection in machine learning can be broadly classified into the following categories:
In statistics and machine learning, feature selection (also known as variable selection, attribute selection, or variable subset selection) is the practice of choosing a subset of relevant features (predictors and variables) for use in a model construction. Feature selection is one amongst the core concepts in machine learning which massively affects the performance of a model. Irrelevant or partially irrelevant features can negatively impact the model performance.
Whatever goes in, comes out.
Azure machine learning supports three types of feature selection, as shown in the following screenshot. Mar 4 · 3 min read. In this process those features which contribute most to the prediction variable are. The machine learning community classifies feature selection into 3 different categories: