[scikit-learn] How to best understand scikit-learn and know its modules and methods?

C W tmrsg11 at gmail.com
Sun Jun 4 19:06:28 EDT 2017

Yes, they make a lot sense. Thanks!

I wanted to ask a follow-up:

> LinearRegression().fit(X, y)
When I do this, where is everything saved? Or does it disappear after I run

Thank you!

On Sun, Jun 4, 2017 at 6:40 PM, Guillaume Lemaitre <g.lemaitre58 at gmail.com>

> Hope it helps. I answered in the original message
> G
> *From: *C W
> *Sent: *Monday, 5 June 2017 00:31
> *To: *scikit-learn at python.org
> *Reply To: *Scikit-learn user and developer mailing list
> *Subject: *[scikit-learn] How to best understand scikit-learn and know
> its modules and methods?
> Dear scikit learn list,
> I am new to scikit-learn. I am getting confused about LinearRegression.
> For example,
> from sklearn.datasets import load_boston
> from sklearn.linear_model import LinearRegression
> boston = load_boston()
> X = boston.data
> y = boston.target
> model1 = LinearRegression()
> model1.fit(X, y)
> print(model.coef)
> I got a few questions:
> 1) When I do model1.fit(X, y), don't I have to save it? Does object model1
> automatically gets trained/updated? Since I don't see any output, how do I
> know what has been done to the model1?
> The model has been fitted (trained in place). model1 will contain all info
> learnt directly. In addition, the output will be a fitted model1 because
> fit return self. Normally, model1.fit(X,y) will print LinearRegression(...)
> 2) Is there a command to see what's masked under sklearn, like
> sklearn.datasets, sklearn.linear_model, and all of it?
> You can check the documentation API. I think that this is the best user
> friendly thing that you can start with.
> 3) Why do we need load_boston() to load boston data? I thought we just
> imported it, so it should be ready to use.
> Load_boston() is a helper function which will load the data. Importing
> load_boston will import the function not the data. Calling the imported
> function will load the data.
> Thank you very much!
> Mike
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