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# 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.

In Python, the library scikit-learn provides an easy way to plot an ROC curve using the function `roc_curve()`.

The first step is to import the library and load the dataset into a pandas dataframe. Then, split the data into training and testing sets, and train the model on the training set.

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.

In this Learn through Codes example, you will learn: How to plot ROC Curve in Python.

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