Archives for Roc-auc curve
AUC-ROC is the valued metric used for evaluating the performance in classification models. The AUC-ROC metric clearly helps determine and tell us about the capability of a model in distinguishing the classes. The judging criteria being - Higher the AUC, better the model. AUC-ROC curves are frequently used to depict in a graphical way the connection and trade-off between sensitivity and specificity for every possible cut-off for a test being performed or a combination of tests being performed. The area under the ROC curve gives an idea about the benefit of using the test for the underlying question. AUC - ROC curves are also a performance measurement for the classification problems at various threshold settings.
The post Understanding the AUC-ROC Curve in Machine Learning Classification appeared first on Analytics India Magazine.
Evaluation of a machine learning model is crucial to measure its performance. Numerous metrics are used in the evaluation of a machine learning model. Selection of the most suitable metrics is important to fine-tune a model based on its performance. In this article, we discuss the mathematical background and application of evaluation metrics in classification…
The post Python Code for Evaluation Metrics in ML/AI for Classification Problems appeared first on Analytics India Magazine.
In this article, we will learn more about the ROC-AUC curve and how we make use of it to compare different machine learning models to select the best performing model.
The post ROC-AUC Curve For Comprehensive Analysis Of Machine Learning Models appeared first on Analytics India Magazine.