**Confusion Matrix:** is a table that is used describe the performance of classification model.

For example our classification model made 100 prediction on whether person has a disease or not.

- Out of 100 prediction, model predicted 70 times yes and 30 times no.
- In Actual result, 55 have a disease and 45 have not a disease.

Let’s build confusion matrix, but first get basic terms for confusion matrix:

**True Positive(TP):** Actual label is yes and model also predict yes.

**True Negative(TN): **Actual label is No and model predict No.

**False Positive(FP):** Actual label is No and model predict Yes. This is called Type I error.

**False Negative(FN):** Actual label is Yes and model predict No. This is called Type II error.

The list of metrics that can be calculated from confusion matrix:

**Accuracy:**(TP + TN)/total = (45 + 20)/100 = 6.5**Misclassification Rate:**(FP + FN)/total = (25 + 10)/100 = 3.5 Or (1- Accuracy) = 0.35**True Positive Rate:**TP/actual_yes = 45/55**True Negative Rate:**TN/actual_no = 20/45**False Positive Rate:**FP/actual no = 10/45**False Negative Rate:**FN/actual_yes = 24/70**Precision :**TP/total predicted_yes = 45/70**Recall:**TP/total actual_yes**Prevalence:**actual_yes/total = 55/100

## Other metrics can be useful for classification model:

**Null error rate:**how many wrong prediction will be there if you predict only majority class like if you predict only yes in above example then null error rate will be 45/100. This can be baseline metric to compare against our classifier.**F1 Score:**harmonic mean of precision and recall.**ROC curve:**plot between True positive rate and False positive rate. Cohen’s Kappa**Important point for newcomers:**Accuracy metric is always not a good metric for predictive models because if one class is dominating 99% times then accuracy of model will be 99% if we predict only one class. Precision and recall are better metrics in such cases, but precision too can be biased by very unbiased class. Keep learning and be innovative.