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Unsupervised Learning in Python  

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Liyi Ang
(@liyi)
Member
Joined: 4 months ago
Posts: 38
November 21, 2018 8:59 pm  

Do you have any questions relating to Unsupervised Learning in Python? Leave them here!


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hanqi
(@hanqi)
Active Member
Joined: 2 months ago
Posts: 13
December 14, 2018 12:19 am  

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?

Similar in this answer https://stackoverflow.com/questions/51459406/apply-standardscaler-in-pipeline-in-scikit-learn-sklearn,

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

This post was modified 1 month ago by hanqi

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