This workflow depicts utilizing the ROC Curve node to showcase the results of one classifier and two classifiers on a plot. We utilized the "Heart Attack Analysis & Prediction Dataset" from Kaggle for this demonstration.
You can easily download and run the workflow directly in your KNIME installation. We recommend that you use the latest version of the KNIME Analytics Platform for optimal performance. It can also be deployed as a Data App in KNIME Business Hub.
In summary, we imported the dataset mentioned above. We then modified the class column values to 0 for a low chance and 1 for a high chance of having a heart attack. We also renamed all the columns using more descriptive names.
To prepare the data for the model trainer and predictor process, we utilized the "Partitioning" node. We used 80% of the sample to train the models and the remaining data to predict the probability of a heart attack.
Our data application includes a Scorer (JavaScript) node for both models and the ROC Curve view for each model. It is possible to switch between the models dynamically within the view.
You can easily download and run the workflow directly in your KNIME installation. We recommend that you use the latest version of the KNIME Analytics Platform for optimal performance. It can also be deployed as a Data App in KNIME Business Hub.
In summary, we imported the dataset mentioned above. We then modified the class column values to 0 for a low chance and 1 for a high chance of having a heart attack. We also renamed all the columns using more descriptive names.
To prepare the data for the model trainer and predictor process, we utilized the "Partitioning" node. We used 80% of the sample to train the models and the remaining data to predict the probability of a heart attack.
Our data application includes a Scorer (JavaScript) node for both models and the ROC Curve view for each model. It is possible to switch between the models dynamically within the view.