Confusion matrix

1. What is in a confusion matrix?
2. What is confusion matrix with example?
3. What is confusion matrix formula?
4. How does confusion matrix work?

What is in a confusion matrix?

A confusion matrix is a summary of prediction results on a classification problem. The number of correct and incorrect predictions are summarized with count values and broken down by each class. This is the key to the confusion matrix.

What is confusion matrix with example?

Confusion Matrix is a useful machine learning method which allows you to measure Recall, Precision, Accuracy, and AUC-ROC curve. Below given is an example to know the terms True Positive, True Negative, False Negative, and True Negative. True Positive: You projected positive and its turn out to be true.

What is confusion matrix formula?

From our confusion matrix, we can calculate five different metrics measuring the validity of our model. Accuracy (all correct / all) = TP + TN / TP + TN + FP + FN. Misclassification (all incorrect / all) = FP + FN / TP + TN + FP + FN. Precision (true positives / predicted positives) = TP / TP + FP.

How does confusion matrix work?

This is where confusion matrices are useful. A confusion matrix presents a table layout of the different outcomes of the prediction and results of a classification problem and helps visualize its outcomes. It plots a table of all the predicted and actual values of a classifier.