What is the difference between scale and fit in sklearn?

Question

I am new to data science I was watching videos on youtube, were one of the trainers was using both scala and the fit on the dataset. I tried to understand the difference between the scala and fit by going through the documentation provided by the trainer. But I didn't find any. Can anyone tell me the difference?

Answer 1

As per the question to explain it is very difficult since you didn't give any link for the video which you were watching. But I have an example to explain the scala and the fit.

Hope you will understand by the example given by me, so the scala which I am using as an example is:

sklearn.preprocessing.scale()

And the fir is as follows:

sklearn.ensemble.GradientBoostingRegressor

The functionality of the scale operator is to transform all the data in a way that all the variables respond at the same scale.

The functionality of the fit is to train the model by the given data.

As per your question you would have through that scale and the fit operator performance the same operation.

In general, data is pre-processed and then the model is fitted. In this example you need to use scale() then use the fit() to build the model.

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