 ### How to optimize the probability threshold % in binary classification

In classification problems, the cost of false positives is almost never the same as the cost of false negatives. As such, if you are optimizing a solution for a business problem where Type 1 and Type 2 errors have a different impact, you can optimize your classifier for a probability threshold value to optimize the custom loss function simply by defining the cost of true positives, true negatives, false positives and false negatives separately. By default, all classifiers have a threshold of 0.5.

See example below using "credit" dataset.

```# Importing dataset
from pycaret.datasets import get_data

credit = get_data('credit')
# Importing module and initializing setup
from pycaret.classification import *
clf1 = setup(data = credit, target = 'default')

# create a model
xgboost = create_model('xgboost')

# optimize threshold for trained model
optimize_threshold(xgboost, true_negative = 1500, false_negative = -5000)``` You can then pass 0.2 as probability_threshold parameter in predict_model function to use 0.2 as a threshold for classifying positive class. See example below:

`predict_model(xgboost, probability_threshold=0.2)` #### More Articles of Aditi Kothiyal:

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