# Model Evaluation Part 1

Part 1 of Model Evaluation is covered in this lesson. Evaluation measures help us accurately judge the model’s performance.

## Model evaluation measures

In this lesson, we will observe model evaluation for classification. We predict a discrete-valued output and it has its own evaluation measures.

### Confusion Matrix

The confusion matrix serves as an evaluation measure for classification problems. The confusion matrix for a binary classification problem having only two class labels (0 and 1) would look like this.

Actual Class Label (1) | Actual Class Label (0) | |
---|---|---|

Predicted Class Label (1) | TP | FP |

Predicted Class Label (0) | FN | TN |

Here

**TP(True Positive)**: We predicted a positive Class Label (1) and it is True.

**TN(True Negative)**: We predicted a negative Class Label (0) and it is True.

**FP(False Positive)**: It is also called Type 1 Error. We predicted a positive Class Label (1) and it is False.

**FN(False Negative)**: It is also called Type 2 Error. We predicted a negative Class Label (0) and it is True.

Note that positive and negative refer to the predicted class labels whereas True and False refer to the actual class labels.

This confusion matrix is the basis of many evaluation measures we are calculating below.

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