A Quick Guide To ROC Curve in Machine Learning and Its Interpretation

Introduction to ROC Machine Learning

Starting with the complete form of the ROC curve, which is the Receiver Operation Characteristic Curve. The ROC curve is a graph that represents the performance of the classification model. The evaluation of the model is one of the most aspects of machine learning. In machine learning, we need to ensure that the model is working correctly or not. The ROC Machine Learning evaluates the threshold level.   

ROC Curve Interpretation

For roc curve interpretation, the more the roc curve to the left side, the more chances the model data will classify the mode. For the ROC curve interpretation, the calculation of the AUC curve tells about the plot location under the curve. 

For a better mode, the AUC should be close to 1. If the AUC is 0.5, it would have a perfect diagonal line and the perfect representation of the random classification. This ROC curve interpretation is used for calculating the AOC curve for the multiple logistic regression model. The ROC curve Interpretation helps us ensure that the best is taken out of the model. 

For example – the three logistics models are taken for ROC curve interpretation for logistic model regression. After doing the calculation of the model, the results for AUC for three models are 

First – 0.923

Second – 0.794

Third – 0.588 

We have already discussed that the AUC should be near 1 in these three models. The first model is nearest to 1, which means the perfect model would be the first one. 

ROC Curve Example

For the ROC Curve example for creating ROC Curve. 

Firstly we have to get the classification model prediction. We would take the ROC Curve example for repaying a loan. The probabilities vary from 0 to 1. The more it is close to 1, the more the probability of repaying the loan. We need to determine the threshold for repaying or won’t repay the loan. In the ROC Curve example, the threshold is 0.35

● The prediction at this point or above shows that would repay the loan 

● The prediction below this punt wud show that w would not repay the loan

For building a confusion matrix. All the positives show the repayment of the loan. 

There are two categories for this the 

The actual negative will show that they will repay the loan 

The false positive will show that w will not repay the loan  

After figuring out the actual positive and false positive rates, we need to see how we do calculate for the classification model. 

ТР – True Positive Rate (TPR) also called sensitivity/recall/hit rate- TP + FN

FP False Positive Rate (FPR), also called fall out- FP + TN

The TPR in this ROC Curve example shows ha people who did repay the loan, and the FPR shows those who didn’t repay the loan. The threshold was 0.35. After doing the calculation, 90% of them did repay the loan, which was the TPR, and 10% didn’t repay the loan, which was FPR. But the result for threshold gets more extensive than TPR, and FPR decreases. If in the ROC Curve example, the threshold is zero, then the TPR and FPR would be 100%, and if the threshold is one, then the TPR and FPR would be 0% 

The last step would be plotting the TPR and FPR on the graph. 

According to the ROC Curve example, we need to calculate many thresholds for getting TPR and FPR. for each ROC Curve example. We keep the FPR on the x-axis and TPR on the y-axis to join the dot to the line. The area below the line is the area under the curve. AUC is significant as it is used to calculate the performance of the classification model. 

AUC ROC Curve Interpretation

Firstly before understanding AUC ROC Curve Interpretation, we need to understand the AUC ROC curve at the different thresholds. The performance of the classification model is found using the AUC ROC model. The ROC curve is the curve for probability, and the AUC represents the degree of separability. The difference between the classes is found using this model. The higher the AUC, the better the model. It is like the better it predicts 0 as 0 and 1 as 1. To plot the ROC Curve, the T and FPR are against each other at the y-axis and x-axis. For the AUC ROC Curve, interpretation is made in a multi-class model. In a multi-class model, for the plotting of the mode N number of AUC ROC Curves for N is the number classes the methodology of one v all are used for the multi-class model. for example, If you have three classes named A, B, and C 

 plotting of the model will be like one ROC for A classified contrary to B and C, a second one ROC for B classified contrary to A and C, and the last one of C classified contrary to B and A

ROC Curve Machine Learning Python

To understand the ROC Curve machine learning python, here are some steps we need to visualize for establishing the ROC Curve. 

The steps for the visualization are – 

1. Import the dependencies
2.  some fake data with the draw data package for Jupyter notebooks are drawn 
3.  to a pandas data frame, fake data is imported. 
4.  logistic regression model on the data is fit 
5.  predict the logistic regression model in the probability values
6. The different threshold scores are set 
7.  roc curve plot visualization is done 
8.  conclusions

These are the step to be done for ROC Curve machine learning python. For a good model, we need to keep varying the model. The TRP should be close to 1, and the FPR should be as low as possible;e for the perfect ROC Curve machine learning python model. Now we would interpret these steps of ROC Curve machine learning python with an example the threshold is 0.35  

Step 1 – import the ROC python library and use the roc_curve() to get the threshold which is TPR and FPR.

Step 2- I would repeat the step for AUC, but the curve name would be auc_curve(), the ROC function for python. 

Step 3 – the ROC curve is plotted.

Step 4 – take a printout of the predicted probabilities for class 1. This is done for the threshold for building a classifier model for the desired outcomes. 

Step 5- the setting is 0.35 for the threshold. 

Step 6 – take a printout of the new confusion matrix. 

Step 7: Print out the other metrics’ performance as well. 

Step 8 – compare the performances of the threshold. 

Conclusion 

ROC machine learning is a straightforward model. It helps the classification model; the better the mode, the better the prediction levels. Through this article, we mentioned the ROC curve examples, which help get an idea of the model and make the model easy to understand. The AUC ROC interpretation helps in the classification of the model in a better way. The more the AUC, the model will better.