A Beginner’s Guide to ROC and AUC Curves. In this article, we will go through another important evaluation metric AUC-ROC score. What is AUC-ROC.
As can be seen in the title above, the purpose of this blog is to gain a basic but strong fundamental knowledge of ROC and AUC and how they relate to a binary classification model. The order of this blog is the following.
To put it simply, ROC(receiver operating characteristic curve) and AUC (area under the curve) are measures used to evaluate performance of classification models. ROC is a graph that shows the performance for a classification model at all unique thresholds. The graph uses the the following parameters on its axes:
True Positive Rate
False Positive Rate
As you can see in the graphs above, the errors of our classification model are dependent on our threshold selection. Ideally, if we have a threshold and there is no overlap between the Red curve (positive class) and the Green curve (negative class), then our model would be able to perfectly distinguish between the two classes. Thus, we would eliminate type one error (false positive) and type two errors (false negative).
However, in real world examples, this is very unlikely. In real world examples, there is a tradeoff between type 1 and type II errors. As you can see in the first graph, we can increase our threshold to decrease our false positive count. Thus, decreasing our type 1 errors but at the same time we are increasing our count of false negative results. In other words, more type II error. The opposite would happen if we instead decreased our threshold.
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Statistics for Data Science and Machine Learning Engineer. I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
Become a data analysis expert using the R programming language in this [data science](https://360digitmg.com/usa/data-science-using-python-and-r-programming-in-dallas "data science") certification training in Dallas, TX. You will master data...
The List of Top 10 Lists in Data Science; Going Beyond Superficial: Data Science MOOCs with Substance; Introduction to Statistics for Data Science; Content-Based Recommendation System using Word Embeddings; How Natural Language Processing Is Changing Data Analytics. Also this week: The List of Top 10 Lists in Data Science; Going Beyond Superficial: Data Science MOOCs with Substance; Introduction to Statistics for Data Science