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What is feature scaling in machine learning?
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.
Should you scale the test set?
The test set must use identical scaling to the training set. And the point is given that: Do not scale the training and test sets using different scalars: this could lead to random skew in the data.
Why is feature scaling only to training set?
As with all the transformations, it is important to fit the scalers to the training data only, not to the full dataset (including the test set). Only then can you use them to transform the training set and the test set (and new data)
How to scaling training set and test data?
Using normalization, you make the scale of them the same as each other, helps accelerate the learning process. You should find the mean and variance for each feature separately on your training data. then during training and testing each feature should be reduced by the corresponding mean and be divided by the corresponding standard deviation.
How to scale train validation and test sets properly?
How to scale train, validation and test sets properly using StandardScaler? Some articles says that in case of having only train and test sets, first, we need to use fit_transform () to scale training set and then only transform () for test set, in order to prevent data leakage. In my case, I have also validation set.
How does a feature scaling estimator scale data?
Scale each feature by its maximum absolute value. This estimator scales and translates each feature individually such that the maximal absolute value of each feature in the training set is 1.0. It does not shift/center the data and thus does not destroy any sparsity.