How to plot ROC Curve in Python
When building a machine learning model for a binary classification problem, it’s important to evaluate its performance using various metrics. One way to do this is by plotting an ROC (Receiver Operating Characteristic) curve.
An ROC curve is a graph that shows the relationship between the model’s true positive rate (sensitivity) and its false positive rate (1-specificity) at different classification thresholds. It helps to understand how the model’s performance changes as the threshold is adjusted.
After that, use the
roc_curve() function, which takes the actual class labels and the predicted class probabilities as inputs. The function returns three arrays: the false positive rate, true positive rate, and thresholds.
You can use the matplotlib library to plot the ROC curve, by plotting the false positive rate against the true positive rate.
Additionally, you can use
roc_auc_score() function to calculate the area under the ROC curve which is a measure of the model’s performance, higher the area under the curve, better the model.
In summary, an ROC curve is a powerful tool for evaluating the performance of a machine learning model for a binary classification problem. By using the roc_curve() function in scikit-learn, it’s easy to plot an ROC curve in Python, making it a valuable tool for data scientists and machine learning practitioners.
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