Weighted Logistic Regression In Python

I'm looking for a good implementation for logistic regression (not regularized) in Python. I'm looking for a package that can also get weights for each vector. Can anyone suggest a

Solution 1:

I notice that this question is quite old now but hopefully this can help someone. With sklearn, you can use the SGDClassifier class to create a logistic regression model by simply passing in 'log' as the loss:

sklearn.linear_model.SGDClassifier(loss='log', ...).

This class implements weighted samples in the fit() function:

classifier.fit(X, Y, sample_weight=weights)

where weights is a an array containing the sample weights that must be (obviously) the same length as the number of data points in X.

See http://scikit-learn.org/dev/modules/generated/sklearn.linear_model.SGDClassifier.html for full documentation.

Solution 2:

The “balanced” mode uses the values of y to automatically adjust weights inversely proportional to class frequencies in the input data as n_samples / (n_classes * np.bincount(y))

from sklearn.linear_model importLogisticRegressionmodel= LogisticRegression(class_weight='balanced')

model = model.fit(X, y)

EDIT

Sample Weights can be added in the fit method. You just have to pass an array of n_samples. Check out documentation -

http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html#sklearn.linear_model.LogisticRegression.fit

Hope this does it...

Solution 3:

I think what you want is statsmodels. It has great support for GLM and other linear methods. If you're coming from R, you'll find the syntax very familiar.

statsmodels weighted regression

getting started w/ statsmodels

Solution 4:

Have a look at scikits.learn logistic regression implementation

Solution 5:

Do you know Numpy? If no, take a look also to Scipy and matplotlib.