Which Classification Metric Should You Use?

You will learn useful statistical metrics to evaluate classification metrics based on pratical examples, and which one should you use to choose the best model during the model optimization phase. 0:00 Introduction 0:35 A very accurate COVID-19 test 1:05 Confusion Matrix 3:51 What types of Machine Learning are most common? 5:20 Supervised learning techniques 6:15 True Positives, True Negatives, False Positives, False Negatives 6:58 When to optimize for Sensitivity or Specificity 9:18 When to optimize for Recall or Precision 10:30 ROC curve and Interactive ROC curve 14:04 Which metric should you use for Unbalanced Datasets? F1 and Balanced accuracy 15:35 Cohen's Kappa value and Matthew's Correlation Coefficient (MCC) 17:43 The Rotten Egg Test 23:55 Which metric should you use? 24:55 The metrics should you report for classification #MachineLearning #Classification #DataScience