learn-co-curriculum / dsc-enterprise-hsbc-roc-curves-and-auc-lab

ROC Curves and AUC - Lab

Introduction

In this lab, you'll practice drawing ROC graphs, calculating AUC, and interpreting these results. In doing so, you will also further review logistic regression, by briefly fitting a model as in a standard data science pipeline.

Objectives

You will be able to:

• Evaluate classification models using various metrics
• Define and understand ROC and AUC

Training the Model

Start by repeating the previous modeling steps we have discussed. For this problem, you are given a dataset mushrooms.csv. Your first job is to train a LogisticRegression classifier on the dataset to determine whether the mushroom is edible or poisonous. The first column of the dataset class indicates whether or not the mushroom is poisonous or edible.

** For consistency use random_state=0**

#Your code here

ROC Metrics

Next, calculate the false positive rate and true positive rate (you can use the built-in metrics from sci-kit learn) of your classifier.

# Your code here

Drawing the ROC Graph

Next, use the false positive rate and true positive rate to plot the Receiver Operating Characteristic Curve for both the train and test sets.

# Your code here

Interpretation:

What do you notice about these ROC curves?

Your answer here

Interpretation

Look at the ROC curve graph from the lesson:

Think about the scenario of this model: predicting heart disease. If you tune the current model to have an 82% True Positive Rate, (you've still missed 20% of those with heart disease), what is the False positive rate?

fpr = #write the approximate fpr when tpr=.8

Interpretation 2

If you instead tune the model to have a 95.2% True Postive Rate, what will the False Postive Rate be?

fpr = #write the approximate fpr when tpr=.95

Opinion

In the case of heart disease dataset that we've been talking about, do you find any of the above cases acceptable? How would you tune the model? Describe what this would mean in terms of the number of patients falsely scared of having heart disease and the risk of missing the warning signs for those who do actually have heart disease.

Your answer here

Summary

In this lab you further explored ROC curves and AUC, drawing graphs and then interpreting these results to lead to a more detailed and contextualized understanding of your model's accuracy.