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GLM-Net regression (GLMNET) Classifier.
GLM-Net is the model selection algorithm from:
Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Models via Coordinate Descent. http://www-stat.stanford.edu/~hastie/Papers/glmnet.pdf
To make use of GLMNET, you must have R and RPy installed as well as both the glmnet contributed package. You can install the R and RPy with the following command on Debian-based machines:
sudo aptitude install python-rpy python-rpy-doc r-base-dev
You can then install the glmnet package by running R as root and calling:
install.packages()
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_clf_internals = ['glmnet', 'linear', 'has_sensitivity', 'does Describes some specifics about the classifier -- is that it is doing regression for instance.... |
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family = Parameter('gaussian', allowedtype= 'basestring', choi
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alpha = Parameter(1.0, min= 0.01, max= 1.0, allowedtype= 'floa
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nlambda = Parameter(100, allowedtype= 'int', min= 1, doc= """M
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standardize = Parameter(True, allowedtype= 'bool', doc= """Whe
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thresh = Parameter(1e-4, min= 1e-10, max= 1.0, allowedtype= 'f
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pmax = Parameter(None, min= 1, allowedtype= 'None or int', doc
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maxit = Parameter(100, min= 10, allowedtype= 'int', doc= """Ma
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model_type = Parameter('covariance', allowedtype= 'basestring'
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weights = property(lambda self: self.__weights)
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Initialize GLM-Net. See the help in R for further details on the parameters
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data (Dataset).
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_clf_internalsDescribes some specifics about the classifier -- is that it is doing regression for instance....
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family
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alpha
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nlambda
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standardize
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thresh
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pmax
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maxit
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model_type
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