In machine learning, metrics are essential for evaluating model performance. Here's a guide to key metrics:

🎯 Accuracy

  • Measures the ratio of correct predictions to total predictions.
  • Formula: (True Positives + True Negatives) / (Total Predictions)
  • Accuracy

🔍 Precision

  • Focuses on the accuracy of positive predictions.
  • Formula: True Positives / (True Positives + False Positives)
  • Precision

📌 Recall

  • Evaluates the model's ability to capture all positive instances.
  • Formula: True Positives / (True Positives + False Negatives)
  • Recall

🧩 F1 Score

  • Balances Precision and Recall using harmonic mean.
  • Formula: 2 * (Precision * Recall) / (Precision + Recall)
  • F1_Score

📈 AUC-ROC Curve

  • Represents the area under the Receiver Operating Characteristic curve.
  • ROC_Curve

For deeper insights, explore our Machine Learning Tutorial. 🚀