Analytics for the Internet of Things (IoT)

Precision, recall, and specificity

The results of testing the effectiveness of an ML model against test data can be summed up into four categories (assuming the model is predicting between two classes, such as yes/no or broken/not broken). We will use the example of a wearable IoT sensor where the sensor data is being used to predict if someone is walking or not walking:

1. The ML model said the person was walking, but he was actually sitting on the couch watching Dancing with the Stars drinking a super-sized Sprite. This is a False Positive (FP), also called a Type I error.
2. The ML model said he was not walking when he, in fact, was walking quite quickly to the restroom due to the super-sized Sprite he just drank. This is a False Negative (FN) ...