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In this video on the confusion matrix, we’ll dive deep into what a confusion matrix is. You'll learn how this essential tool helps summarize the performance of a classification model by comparing actual and predicted outcomes. We’ll also explore why confusion matrices are so important in evaluating and improving your models. Next, we’ll break down the parts of the confusion matrix—True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). These elements are foundational to understanding your model’s strengths and weaknesses.We’ll also cover the confusion matrix metrics such as accuracy, precision, recall, and F1-score. These metrics provide valuable insights into your model’s performance, helping you make informed decisions on model adjustments. Finally, we’ll bring it all together with a hands-on demo where you’ll see a confusion matrix in action with a real dataset. This practical example will help you understand how to implement and interpret confusion matrices in your projects.
✅ What is Type 1 and Type 2 error in confusion matrix?
A Type-1 error, also called a false positive, occurs when a correct null hypothesis is incorrectly rejected. On the other hand, a Type-2 error, or false negative, happens when we fail to reject a null hypothesis that is actually false.
✅ What is TP FP TN FN?
A confusion matrix is a table that provides an overview of a classification model's performance by comparing the predicted labels to the actual labels. It shows the counts of true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) based on the model's predictions.
✅ What is the formula for confusion matrix?
accuracy = (TP + TN) / (TP + TN + FP + FN) sensitivity, aka true positive rate = TP / (TP + FN) specificity, aka true negative rate = TN / (TN + FP) positive predictive value (PPV), aka precision = TP / (TP + FP)
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