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Feature Scaling ? The last step of Feature Engineering Feature Scaling is the process of scaling or converting all the values in our dataset to a given scale. Some machine learning algorithms like linear regression, logistic regression, etc use gradient descent optimization.

Feature transformation is a mathematical transformation in which we apply a mathematical formula to a particular column (feature) and transform the values, which are useful for our further analysis.

Feature Transformation is simply a function that transforms features from one representation to another. Feature Scaling is a technique of converting all the values of a feature in the same range.

What is Feature Transformation? 1. It is a technique by which we can boost our model performance. Feature transformation is a mathematical transformation in which we apply a mathematical formula to a particular column(feature) and transform the values which are useful for our further analysis.

Feature transformation: transformation of data to improve the accuracy of the algorithm; feature selection: removing unnecessary features.

Scaling just changes the range of your data. Normalization is a more radical transformation. The point of normalization is to change your observations so that they can be described as a normal distribution.

Feature transformation allows for the maximum benefit of the dataset's features and the long-term success of the application or model. Applying various mathematical techniques to existing application features can result in new features or feature reduction.

Feature scaling is a method used to normalize the range of independent variables or features of data. In data processing, it is also known as data normalization and is generally performed during the data preprocessing step.