- Credit Risk Modelling - Case Studies
- Classification vs. Regression Models
- Case Study - German Credit - Steps to Build a Predictive Model
- Import Credit Data Set in R
- German Credit Data : Data Preprocessing and Feature Selection in R
- Credit Modelling: Training and Test Data Sets
- Build the Predictive Model
- Logistic Regression Model in R
- Measure Model Performance in R Using ROCR Package
- Create a Confusion Matrix in R
- Credit Risk Modelling - Case Study- Lending Club Data
- Explore Loan Data in R - Loan Grade and Interest Rate
- Credit Risk Modelling - Required R Packages
- Loan Data - Training and Test Data Sets
- Data Cleaning in R - Part 1
- Data Cleaning in R - Part 2
- Data Cleaning in R - Part 3
- Data Cleaning in R - Part 5
- Remove Dimensions By Fitting Logistic Regression
- Create a Function and Prepare Test Data in R
- Building Credit Risk Model
- Credit Risk - Logistic Regression Model in R
- Support Vector Machine (SVM) Model in R
- Random Forest Model in R
- Extreme Gradient Boosting in R
- Predictive Modelling: Averaging Results from Multiple Models
- Predictive Modelling: Comparing Model Results
- How Insurance Companies Calculate Risk

# Create a Confusion Matrix in R

A confusion matrix is a tabular representation of Actual vs Predicted values.

As you can see, the confusion matrix avoids "confusion" by measuring the actual and predicted values in a tabular format. In table above, Positive class = 1 and Negative class = 0. Following are the metrics we can derive from a confusion matrix:

**Accuracy** - It determines the overall predicted accuracy of the model. It is calculated as Accuracy = (True Positives + True Negatives)/(True Positives + True Negatives + False Positives + False Negatives)

**True Positive Rate (TPR)** - It indicates how many positive values, out of all the positive values, have been **correctly predicted**. The formula to calculate the true positive rate is (TP/TP + FN). Also, TPR = 1 - False Negative Rate. It is also known as **Sensitivity** or **Recall**.

**False Positive Rate (FPR)** - It indicates how many negative values, out of all the negative values, have been **incorrectly predicted**. The formula to calculate the false positive rate is (FP/FP + TN). Also, FPR = 1 - True Negative Rate.

**True Negative Rate (TNR)** - It indicates how many negative values, out of all the negative values, have been **correctly predicted**. The formula to calculate the true negative rate is (TN/TN + FP). It is also known as **Specificity**.

**False Negative Rate (FNR)** - It indicates how many positive values, out of all the positive values, have been incorrectly predicted. The formula to calculate false negative rate is (FN/FN + TP).

**Precision:** It indicates how many values, out of all the predicted positive values, are actually positive. It is formulated as:(TP / TP + FP).

**F Score:** F score is the harmonic mean of precision and recall. It lies between 0 and 1. Higher the value, better the model. It is formulated as 2((precision*recall) / (precision+recall)).

We can create the confusion matrix for our data.

```
> confusionMatrix(credit_test$Creditability,pred_value_labels)
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 48 32
1 59 161
Accuracy : 0.6967
95% CI : (0.6412, 0.7482)
No Information Rate : 0.6433
P-Value [Acc > NIR] : 0.02975
Kappa : 0.2996
Mcnemar's Test P-Value : 0.00642
Sensitivity : 0.4486
Specificity : 0.8342
Pos Pred Value : 0.6000
Neg Pred Value : 0.7318
Prevalence : 0.3567
Detection Rate : 0.1600
Detection Prevalence : 0.2667
Balanced Accuracy : 0.6414
'Positive' Class : 0
```

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