Tag Archives: Confusion Matrix

ML Zoomcamp 2023 – Evaluation metrics for classification– Part 4

Precision & Recall Precision and Recall are essential metrics for evaluating binary classification models. Precision measures the fraction of positive predictions that were correct. In other words, it quantifies how accurately the model predicts customers who are likely to churn. Precision = True Positives / (# Positive Predictions) = True Positives / (True Positives +Continue reading “ML Zoomcamp 2023 – Evaluation metrics for classification– Part 4”

ML Zoomcamp 2023 – Evaluation metrics for classification– Part 3

Confusion table / matrix Different types of errors and correct decisions In this section, we’ll discuss the confusion matrix, a vital tool for evaluating the performance of binary classification models. The confusion matrix allows us to examine the various errors and correct decisions made by our model. As we’ve previously discussed, class imbalance can significantlyContinue reading “ML Zoomcamp 2023 – Evaluation metrics for classification– Part 3”