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Unsupervised Learning in Python
Do you have any questions relating to Unsupervised Learning in Python? Leave them here!
The pipe documentation https://scikit-learn.org/stable/modules/generated/sklearn.pipeline.make_pipeline.html does not show which pipe methods are available, how do i know how to use the pipe? Should i be looking at sklearn.pipeline.Pipeline methods instead and assume all the methods there apply to sklearn.pipeline.make_pipeline too?
Also, can someone explain what's going under the hood in pipeline? The application of pipe methods onto each object inside the pipe seems inconsistent.
From the lesson:
pipeline = make_pipeline(scaler, kmeans) pipeline.fit(samples) pipeline.predict(samples)
I was expecting to see a transform or a fit_transform from the scaler after fit but it's nowhere in the above code?
how did the person replying come up with this implied workflow of the pipe? Is it documented anywhere? i am trying to understand if there is a rule of thumb to think about which methods are being applied to which class in the pipe, in what order, and if there are repetitions/restarting from an earlier class in the pipe when moving from training-->testing data
Step 0: The data are split into TRAINING data and TEST data according to the cv parameter that you specified in the GridSearchCV.
Step 1: the scaler is fitted on the TRAINING data
Step 2: the scaler transforms TRAINING data
Step 3: the models are fitted/trained using the transformed TRAINING data
Step 4: the scaler is used to transform the TEST data
Step 5: the trained models predict using the transformed TEST data