About ROC and AUC ROC curve is a graph representing various confusion matrices for a particular machine learning model. For given two ROC curves of a two different machine learning models, the one that has a greater AUC is considered to be a better machine learning model for the given problem statement at hand.
Interactive web application using python for understanding AUC and ROC. In this web application, you can toggle the slidebars to generate two normal distributions for positive and negative samples and check how your model performs given these normal distibution to perform the classification. There are two models that are used to make predictions, they are Gaussian Naive Bayes Classifier and Logistic Regression model. ROC curves for both the models are generated, and one can study the performance of the model to understand which model is better over other.
Dash, Plotly, Sklearn, ROC, AUC, Python, Numpy
This project is to aid those who want to gain visual understanding of AUC and ROC.
A screenshot of the web application:
- Clone the project
- Ensure that you have numpy, plotly and dash installed on your pc, in case they are absent, you can install using 'pip install ' or if you are using anaconda command prompt you can install using 'conda install '.
- navigate to the app.py file and run the file using 'python app.py'
- open the web browser and go to localhost, 'http://127.0.0.1:8050/' or whichever location is shown when you run the above command.
- toggle the variables and learn.
- More strutured notes.
- Building Multiclass ROC curves
- MIT License added