My ML Model Was 97% Confident Every Time — Here’s Why That Was Actually a Problem

1. 1Build a simple ML model to predict a continuous outcome
2. 2Run the model and evaluate its performance using metrics like accuracy and confidence intervals
3. 3Test the model on a holdout dataset to check for overfitting
4. 4Apply techniques like regularization or early stopping to reduce overconfidence
5. 5Compare the model's performance to a baseline model or a human benchmark

Data scientists and machine learning engineers can benefit from understanding the pitfalls of overconfident models, while product managers and business stakeholders should be aware of the potential risks of relying on overly confident predictions

💡 Overconfidence in ML models can be a sign of overfitting or poor calibration, leading to poor generalization and decision-making

🚨 97% confidence doesn't always mean accuracy 🚨