Playing With Probability Cut-off
Explore how altering probability cutoffs affects decision tree classification outcomes. Understand the trade-offs between false negatives and false positives, especially in critical applications like medical screening. Learn to analyze confusion matrices and classification reports to optimize model predictions and prepare the final model for business deployment.
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Let’s consider that we are satisfied with our model after tuning it with the grid search module. The question is, Should we stay with the probability cutoff of 0.5 for the class prediction?
Overview
Sometimes, we might be interested in different probability cutoffs for the class predictions, typically in medical-related studies. We want to bring the high-risk patients for screening at a very early stage to avoid ...