Скачать или смотреть Complete guide to the ROC Curve | AUC (Area Under the Curve) | RIGHT threshold | Data Science

In this video, we'll walk you through what the ROC curve is, how it can be used as a model performance measure, and its role in helping you find the right threshold for your model.

We start by explaining the fundamental concept of a threshold, which is used to convert predicted probabilities into class labels. To illustrate this, we'll use an example that demonstrates how changing the threshold values between 0, 0.5, and 1 😮 affects the calculation of True Positive Rate (TPR) and False Positive Rate (FPR) 📈.

Before diving into ROC curves, we'll provide a brief recap of the confusion matrix and its components 🧩, making it easier for you to follow along.

The heart of the video is the ROC curve itself, which showcases the relationship between TPR and FPR over a range of thresholds. You'll learn how to distinguish between a random classifier (the diagonal line) 🎲 and a well-performing model with a good Area Under the Curve (AUC) 📈.

We'll also demonstrate how to determine the right threshold for a specific model 🎯, allowing you to strike the perfect balance between sensitivity and specificity based on your application's needs.

Finally, we'll guide you through creating an ROC curve using Plotly in Python 🐍, making it an interactive and engaging experience that helps you visualize and understand your model's performance 📈📊.

This video provides a comprehensive introduction to ROC curves, helping you make informed decisions when evaluating and fine-tuning your predictive models. 🤓👨‍💻📈

Happy Learning!