Master the key evaluation metrics for machine learning! From Accuracy to Log-loss, discover which metrics you need for proper model evaluation. Whether you’re dealing with imbalanced data or regression tasks, we’ve got you covered! 🚀
🔹 Accuracy – Works for balanced data.
🔹 Precision & Recall – Crucial in fraud detection & medical diagnoses.
🔹 F1-Score & ROC-AUC – Balance precision and recall, measure discrimination.
🔹 PR-AUC & MCC – Best for imbalanced datasets.
🔹 MAE, MSE, R² – Essential for regression tasks.
🔹 Log-Loss – Penalizes incorrect confident predictions.
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#MachineLearning #MLEvaluation #DataScience #TechShorts #PrecisionRecall #F1Score #LogLoss #DataAnalysis #ModelEvaluation #AI #DeepLearning
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