Scikit-learn model parameters unavailable? If so what ML workbench alternative?
machine-learning, python, scikit-learn
Solution
The fitted model parameters are stored directly as attributes on the model instance. There is a specific naming convention for those fitted parameters: they all end with a trailing underscore as opposed to user-provided constructor parameters (a.k.a. hyperparameters) which don't.
The type of the fitted attributes is algorithm-dependent. For instance for a kernel Support Vector Machine you will have the arrays support vectors, dual coefs and intercepts while for random forests and extremly randomized trees you will have a collection of binary trees (internally represented in memory as contiguous numpy arrays for performance matters: structure of arrays representation).
See the Attributes section of the docstring of each model for more details, for instance for SVC:
http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html#sklearn.svm.SVC
For tree based models you also have a helper function to generate a graphivz_export of the learned trees:
http://scikit-learn.org/stable/modules/tree.html#classification
To find the importance of features in forests models you should also have a look at the `compute_importances` parameter, see the following examples for instance:
http://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html#example-ensemble-plot-forest-importances-py
http://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances_faces.html#example-ensemble-plot-forest-importances-faces-py
Problem
I am doing machine learning using scikit-learn as recommended in this question. To my surprise, it does not appear to provide access to the actual models it trains. For example, if I create an SVM, linear classifier or even a decision tree, it doesn't seem to provide a way for me to see the parameters selected for the actual trained model. Seeing the actual model is useful if the model is being created partly to get a clearer picture of what features it is using (e.g., decision trees). Seeing the model is also a significant issue if one wants to use Python to train the model and some other code to actually implement it. Am I missing something in scikit-learn or is there some way to get at this in scikit-learn? If not, what is the a good free machine learning workbench, not necessarily in python, in which models are transparently available?