How to scaling training set and test 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.

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 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.

When to check for data leakage in machine learning?

Once you have completed your modeling process and actually created your final model, evaluate it on the validation dataset. This can give you a sanity check to see if your estimation of performance has been overly optimistic and has leaked.

How to normalize training and test data at the same time?

The right way to do this is to use only the training set to calculate the mean and variance, normalize the training set, and then at test time, use that same (training) mean and variance to normalize the test set.

How to scale training and validation data in real time?

Divide the sample data in training and validation set. Scale training data. Using same factor as training data (example mean and variance of training data) scale test data. For in production prediction in real time use the above stored value to scale the feature.