### Goodness of Fit for a Logistic Regression

- Classification Matrix
- Accuracy

### Classification Table & Accuracy

Predicted / Actual | 0 |
1 |
---|---|---|

0 |
True Positive (TP) | False Positive (FP) |

1 |
False Negative (FN) | True Negative (TN) |

- Also known as confusion matrix
`\(Accuracy=\frac{(TP+TN)}{(TP+FP+FN+TN)}\)`

### Classification Table in R

```
threshold=0.5
predicted_values<-ifelse(predict(prod_sales_Logit_model,type="response")>threshold,1,0)
actual_values<-prod_sales_Logit_model$y
conf_matrix<-table(predicted_values,actual_values)
conf_matrix
```

```
## actual_values
## predicted_values 0 1
## 0 257 3
## 1 5 202
```

### Accuracy in R

```
accuracy<-(conf_matrix[1,1]+conf_matrix[2,2])/(sum(conf_matrix))
accuracy
```

`## [1] 0.9828694`