Dimensionality Reduction Techniques
Feature selection and feature extraction are the two primary categories of dimensionality reduction approaches. A subset of the original characteristics that are most pertinent or significant to the current issue are chosen through feature selection. Feature extraction entails merging or otherwise altering the source features to produce new features. Some of the popular feature selection methods are:
Filter techniques: These approaches, such as correlation, variance, or information gain, rank the characteristics according to how relevant they are to the target variable. The highest-scoring elements are chosen, while the remainder are disregarded.
Wrapper methods: The “wrapper” approach chooses features based on how well the model performs. They experiment with various feature combinations and assess how well they match the model. The attributes that provide the best model are chosen, and the others are disregarded.
Embedded methods: Techniques that incorporate feature selection and model training are referred to as embedded techniques. To choose the characteristics that are most advantageous for the model, they employ regularization methods like decision trees.
Model with Reduction Methods
Machine learning models are now more powerful and sophisticated than ever before, able to handle challenging problems and enormous datasets. But with great power also comes huge complexity, and occasionally these models grow too complicated to be useful for implementation in the real world. Methods of model reduction are useful in this situation. This article will discuss the idea of model reduction in machine learning, explaining it simply for newcomers, clarifying essential terms, and providing concrete Python examples to show how it works. We will introduce some common dimensionality reduction techniques and show how to apply them to a machine-learning model using Python.
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