Sklearn Logistic Regression - Adjust Cutoff Point

I have a logistic regression model trying to predict one of two classes: A or B. My model's accuracy when predicting A is ~85%. Model's accuracy when predicting B is ~50%. Predict

Solution 1:

As mentioned in the comments, procedure of selecting threshold is done after training. You can find threshold that maximizes utility function of your choice, for example:

from sklearn import metrics
preds = classifier.predict_proba(test_data)
tpr, tpr, thresholds = metrics.roc_curve(test_y,preds[:,1])
print (thresholds)

accuracy_ls = []
for thres in thresholds:
    y_pred = np.where(preds[:,1]>thres,1,0)
    # Apply desired utility function to y_preds, for example accuracy.
    accuracy_ls.append(metrics.accuracy_score(test_y, y_pred, normalize=True))

After that, choose threshold that maximizes chosen utility function. In your case choose threshold that maximizes 1 in y_pred.