The Accuracy Metric
In this lesson I introduce one of the most common metrics in machine learning: the accuracy. The lesson covers how accuracy can be unreliable when the dataset is biased, the meaning of false positives and false negatives, and how to interpret the confusion matrix in real-world scenarios.
Previous Module
Predict Taxi Fares In New York
This Module
Binary Classification
Introducing Binary Classification
Binary Patters In Data
The Threshold Value
The Sigmoid Function
The Binary Confusion Matrix
The Accuracy Metric
The Precision And The Recall
The True Positive Rate (TPR) And False Positive Rate (FPR)
The Receiver Operating Characteristic (ROC) Curve
The Area Under Curve (AUC) Metric
The Calibration Curve