Controlling the threshold in Logistic Regression in Scikit Learn

Question

I am using the LogisticRegression() method in scikit-learn on a highly unbalanced data set. I have even turned the class_weight featu

Answer 1

Yes, Sci-Kit learn is using a threshold of P>0.5 for binary classifications. I am going to build on some of the answers already posted with two options to check this:

One simple option is to extract the probabilities of each classification using the output from model.predict_proba(test_x) segment of the code below along with class predictions (output from model.predict(test_x) segment of code below). Then, append class predictions and their probabilities to your test dataframe as a check.

As another option, one can graphically view precision vs. recall at various thresholds using the following code.

### Predict test_y values and probabilities based on fitted logistic 
regression model

pred_y=log.predict(test_x) 

probs_y=log.predict_proba(test_x) 
  # probs_y is a 2-D array of probability of being labeled as 0 (first 
  column of 
  array) vs 1 (2nd column in array)

from sklearn.metrics import precision_recall_curve
precision, recall, thresholds = precision_recall_curve(test_y, probs_y[:, 
1]) 
   #retrieve probability of being 1(in second column of probs_y)
pr_auc = metrics.auc(recall, precision)

plt.title("Precision-Recall vs Threshold Chart")
plt.plot(thresholds, precision[: -1], "b--", label="Precision")
plt.plot(thresholds, recall[: -1], "r--", label="Recall")
plt.ylabel("Precision, Recall")
plt.xlabel("Threshold")
plt.legend(loc="lower left")
plt.ylim([0,1])