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### AUC-ROC Curve in Machine Learning Clearly Explained - Analytics Vidhya

You've built your machine learning model – so what's next? You need to evaluate it and validate how good (or bad) it is, so you can then decide on whether to implement it. That's where the AUC-ROC curve comes in. The name might be a mouthful, but it is just saying that we are calculating the "Area Under the Curve" (AUC) of "Receiver Characteristic Operator" (ROC). I have been in your shoes.

### The Beginners' Guide to the ROC Curve and AUC

In the previous article here, you have understood classification evaluation metrics such as Accuracy, Precision, Recall, F1-Score, etc. In this article, we will go through another important evaluation metric AUC-ROC score. ROC curve (Receiver Operating Characteristic curve) is a graph showing the performance of a classification model at different probability thresholds. ROC graph is created by plotting FPR Vs. TPR where FPR (False Positive Rate) is plotted on the x-axis and TPR (True Positive Rate) is plotted on the y-axis for different probability threshold values ranging from 0.0 to 1.0.

### 6 Metrics You Need to Optimize for Performance in Machine Learning - DZone AI

There are many metrics to measure the performance of your machine learning model depending on the type of machine learning you are looking to conduct. In this article, we take a look at performance measures for classification and regression models and discuss which is better-optimized. Sometimes the metric to look at will vary according to the problem that is initially being solved. The True Positive Rate, also called Recall, is the go-to performance measure in binary/non-binary classification problems. Most of the time -- if not all of the time -- we are only interested in correctly predicting one class.

### Using Operating Thresholds With the BigML Dashboard - DZone AI

The BigML Team is bringing operating thresholds for your classification model evaluations and predictions. As explained in our previous posts, operating thresholds are a way to improve the performance of your models, especially when the objective field contains an imbalanced distribution for your classes.

### The 6 Metrics You Need to Optimize for Performance in Machine Learning

There are many metrics to measure the performance of your model depending on the type of machine learning you are looking to conduct. In this article, we take a look at performance measures for classification and regression models and discuss which is better optimized. Sometimes the metric to look at will vary according to the problem that is initially being solved. The True Positive Rate also called Recall is the go-to performance measure in binary/non-binary classification problems. Most if not all the time, we are only interested in correctly predicting one class.