What, When and Why Feature Scaling for Machine Learning
Feature Scaling data for Machine Learning Models
TechnoFunnel presents the series of article on Machine Learning. In this article we will be talking about the What is feature Scaling and Why do we require Feature Scaling during Machine Learning. We will also discuss about the Normalization and Standardization of Data along with the Implementation of the same using scikit-learn.
What is Feature Scaling ?
Feature Scaling is one of the important pre-processing that is required for standardizing/normalization of the input data. When the range of values are very distinct in each column, we need to scale them to the common level. The values are brought to common level and then we can apply further machine learning algorithm to the input data.
One example for the same could be, that we have different features, in which one of the feature might have data represented in Kilometre, another column might have data represented in Metre and the last column might have data representation in centimetre. Before applying the algorithm to the data, we need to first bring them to the common scale which might be “Metre”, “Kilometre” or “Centimetre” to have effective analysis and prediction.
