# What's the role of thresholds in decision tree and logistic regression classifiers for binary classification decisions?

Hey, I recently came across the concept of using thresholds in `decision tree` and `logistic regression` classifiers, but I’m not quite clear on how they work. Could someone elaborate on their role in making binary classification decisions based on predicted probabilities? I’m eager to understand this concept better.

Thresholds play a crucial role in making binary classification decisions based on predicted probabilities. They act as decision boundaries, allowing us to classify instances as belonging to one class or another. Let me illustrate this with a code snippet:

This code snippet demonstrates the implementation of a decision tree classifier using the iris dataset in scikit-learn. It first extracts the petal length and width features along with the target variable from the dataset. Then, a decision tree classifier with a maximum depth of 2 and Gini criterion is created.

Subsequently, the code makes predictions on new data (with a petal length of 5.0 cm and a petal width of 1.5 cm) and applies a threshold of 0.7 to the probability of the class ‘Virginica’. If the probability of ‘Virginica’ is greater than or equal to 0.7, the code prints ‘Prediction: Virginica’; otherwise, it prints ‘Prediction: Not Virginica’.

Essentially, this code employs a threshold of 0.7 to make a binary classification decision based on the predicted probabilities generated by the decision tree classifier.

After understanding how thresholds are applied in decision tree, let’s explore how they’re utilized in logistics regression algorithm. Here’s a code snippet showcasing the implementation of thresholds with another classifier:

This code loads the` breast cancer dataset` and splits it into training and testing sets. It then creates a `logistic regression` classifier with L1 regularization and trains it on the training data. The classifier is used to make predictions on the test data and applies a threshold of 0.7 to convert the probabilities into binary predictions. Finally, the `classification report` is printed to evaluate the performance of the classifier.