Feature Scaling
Explore feature scaling concepts in machine learning, including normalization and standardization techniques. Understand how scaling benefits gradient descent and distance-based algorithms, and learn to apply these methods to prepare your data for better model performance.
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Feature scaling
Feature scaling comes under feature engineering. Feature scaling refers to the process of normalizing the features, columns, or dimensions. Many machine learning algorithms are sensitive to the scale or magnitude of the features.
Benefits
It has the following benefits:
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It helps in gradient descent-based algorithms to converge faster.
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It helps in distance-based algorithms to give equal weight to each feature while computing the similarity.
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It helps to compare the feature importance.
Distance-based algorithms compute results by using the distance or similarity between instances in the dataset.
Types of feature scaling
There are these two famous types of feature scaling:
Normalization
It involves rescaling the values of features in the range between 0 and 1. It is a good technique to use if someone doesn’t know the distribution of the input columns or if the distribution is not Gaussian. The formula for it is:
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