Hits: 30

# Applied Data Science Coding with Python: How to get Classification AUC ROC

AUC-ROC (Area Under the Receiver Operating Characteristic curve) is a commonly used metric to evaluate the performance of a binary classification model. It is a graphical representation of the model’s ability to distinguish between the two classes, and it can be used to evaluate a model’s overall performance.

In Python, the scikit-learn library provides a built-in function called “roc_auc_score” that can be used to calculate the AUC-ROC of a model. To use it, you first need to import the function from the library and then pass in two arguments: the predicted class probabilities and the true class labels.

The predicted class probabilities are the likelihood scores generated by the model for each class, and the true class labels are the actual class labels of the data points.

The function will then calculate the AUC-ROC score by plotting the true positive rate against the false positive rate for different thresholds of the predicted class probabilities, and then computing the area under the curve.

AUC-ROC is a useful metric because it is insensitive to the imbalance of classes in the data set, unlike accuracy, and it provides a single value that summarizes the model’s performance across all possible thresholds.

In summary, AUC-ROC is a metric used to evaluate the performance of a binary classification model by measuring the area under the curve of the Receiver Operating Characteristic. Python’s scikit-learn library provides a built-in function called “roc_auc_score” that can be used to calculate AUC-ROC, and it is insensitive to class imbalance and provides a single value that summarizes the model’s performance across all possible thresholds.

In this Applied Machine Learning & Data Science Recipe, the reader will learn: How to get Classification AUC ROC.

## Applied Data Science Coding with Python: How to get Classification AUC ROC

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause.The information presented here could also be found in public knowledge domains.

# Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:

**Applied Statistics with R for Beginners and Business Professionals**

**Data Science and Machine Learning Projects in Python: Tabular Data Analytics**

**Data Science and Machine Learning Projects in R: Tabular Data Analytics**

**Python Machine Learning & Data Science Recipes: Learn by Coding**