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What does Imputer transform do?
You use an Imputer to handle missing data in your dataset. Imputer gives you easy methods to replace NaNs and blanks with something like the mean of the column or even median. But before it can replace these values, it has to calculate the value that will be used to replace blanks.
Why do we use fit transform?
The fit method is calculating the mean and variance of each of the features present in our data. The transform method is transforming all the features using the respective mean and variance. We want our test data to be a completely new and a surprise set for our model. The transform method helps us in this case.
What’s the difference between fit, transform and predict?
For Models: 1 fit () – It calculates the parameters/weights on training data (e.g. parameters returned by coef () in case of Linear Regression) and saves them as an internal objects state. 2 predict () – Use the above calculated weights on test data to make the predictions 3 transform () – Cannot be used 4 fit_transform () – Cannot be used
What’s the difference between a model and a fit transform?
Note that what I call “model” usually will be a scaler, a tfidf transformer, other kind of vectorizer, a tokenizer… In layman’s terms, fit_transform means to do some calculation and then do transformation (say calculating the means of columns from some data and then replacing the missing values).
Do you use X _ train or fit _ transform?
The correct is to fit ONLY with X_train, because you don’t know “your future data” so you cannot use X_test data for fitting anything! Then you can transform your test data, but separately, that’s why there are different methods.
Which is an example of a Transformers class?
Transformers are for pre-processing before modeling. The Imputer class (like SimpleImputer for filling in missing values) and FeatureSelection classes in sklearn are an example of some transformers. Models are used to make predictions like Linear Regression model, Decision Tree model, Random Forest model etc.