| Hope it helps. I answered in the original message G
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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