End-to-End Machine Learning: roc metric in R

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End-to-End Machine Learning: roc metric in R

When training a machine learning model, it’s important to evaluate its performance to understand how well it will work on new, unseen data. One common way to evaluate the performance of a model for binary classification problems is by using a metric called “Receiver Operating Characteristic” (ROC) curve.

ROC curve is a graphical representation of the true positive rate (sensitivity) against the false positive rate (1-specificity) at different threshold settings. The area under the ROC curve (AUC) is a measure of how well the model can distinguish between the positive and negative classes. A higher AUC value indicates a better model performance.

In R, there are several ways to calculate ROC curve and AUC, and several libraries such as caret, mlr, etc. which provide functions to calculate ROC curve and AUC. Some of the most popular functions are roc(), roc.curve() and auc() that can be used to calculate ROC curve and AUC.

It’s important to note that a ROC curve is a useful tool for evaluating the performance of a binary classification model, by showing the trade-off between the sensitivity and specificity of the model. AUC is a single number summary of the classifier performance, a higher AUC value indicates a better model performance.

Overall, ROC curve and AUC are powerful and widely used metrics for evaluating the performance of a machine learning model for binary classification problems. It provides a graphical representation of the true positive rate against the false positive rate and AUC is a single number summary of the classifier performance, a higher AUC value indicates a better model performance. It’s important to use ROC curve and AUC to evaluate a model’s performance in addition to other metrics.

 

In this Applied Machine Learning & Data Science Recipe (Jupyter Notebook), the reader will find the practical use of applied machine learning and data science in R programming: End-to-End Machine Learning: roc metric in R.



End-to-End Machine Learning: roc metric in R

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