Question

• I fit a Logistic Regression Model and train the model based on training dataset using the following

import scikits as sklearn
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression(C=0.1, penalty='l1')
model = lr.fit(training[:,0:-1], training[:,-1)

• I have a cross validation dataset which contains a labels associated in input matrix and can be accessed as

cv[:,-1]

• I run my cross validation dataset against the trained model which returns me the list of 0s and 1s based on prediction

cv_predict = model.predict(cv[:,0:-1])

Question

I want to calculate the precision and recall scores based on acutal labels and predicted labels. Is there a standard method to do it using numpy/scipy/scikits?

Thank you

Answer 1

Yes there are, see the documentation: http://scikit-learn.org/stable/modules/classes.html#classification-metrics

You should also have a look at the sklearn.metrics.classification_report utility:

>>> from sklearn.metrics import classification_report
>>> from sklearn.linear_model import SGDClassifier
>>> from sklearn.datasets import load_digits

>>> digits = load_digits()
>>> n_samples, n_features = digits.data.shape
>>> n_split = n_samples / 2

>>> clf = SGDClassifier().fit(digits.data[:n_split], digits.target[:n_split])

>>> predictions = clf.predict(digits.data[n_split:])
>>> expected = digits.target[n_split:]

>>> print classification_report(expected, predictions)
             precision    recall  f1-score   support

          0       0.90      0.98      0.93        88
          1       0.81      0.69      0.75        91
          2       0.94      0.98      0.96        86
          3       0.94      0.85      0.89        91
          4       0.90      0.93      0.91        92
          5       0.92      0.92      0.92        91
          6       0.92      0.97      0.94        91
          7       1.00      0.85      0.92        89
          8       0.71      0.89      0.79        88
          9       0.89      0.83      0.86        92

avg / total       0.89      0.89      0.89       899