Step 3: Use the right evaluation metrics to determine how well your model makes predictions

“Headlines about machine learning promise godlike predictive power… It’s all a lie.”

Focus on metrics such as “lift,” which calculates the ratio by which your model performs better than making guesses without the model, and “cost,” which calculates the price of false negatives (FN) and false positives (FP). The “fatal flaw” of using accuracy models to evaluate your model is that it treats FNs and FPs as equally bad, when in reality, one may be costlier than the other.