Controlling the threshold in Logistic Regression in Scikit Learn
classification, logistic-regression, machine-learning, python, scikit-learn
Solution
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])
Problem
I am using the `LogisticRegression()` method in `scikit-learn` on a highly unbalanced data set. I have even turned the `class_weight` feature to `auto`. I know that in Logistic Regression it should be possible to know what is the threshold value for a particular pair of classes. Is it possible to know what the threshold value is in each of the One-vs-All classes the `LogisticRegression()` method designs? I did not find anything in the documentation page. Does it by default apply the `0.5` value as threshold for all the classes regardless of the parameter values?