8. Classification
Classification is the task of predicting /estimating a discrete category (the class) from input data. Training is performed using supervised machine learning, using a labeled dataset. Classification can be binary or multi-class.
Sometimes a single model has multiple outputs, multi-label classification. This is not currently supported in emlearn. Instead one classification model per output must be used.
8.1. Applications
Detecting events using Machine Learning has a wide range of applications, and it is commonly performed on sensor data using embedded systems.
Area |
Task |
Sensor |
|---|---|---|
Health |
Detection of heart rythm irreguarity |
Electrocardiogram (ECG) |
Wearables |
Scene classification for contextual awareness |
Sound |
Farming |
Cattle behavior classification for health tracking |
Accelerometer |
Robotics |
Material identification for grippers |
Capacitive |
Buildings |
Human presence detection |
Radar |
8.2. Classification models
Algorithm |
Implementation |
|---|---|
RandomForest |
|
ExtraTrees |
|
DecisionTree |
|
Multi-Layer-Perceptron |
|
Gaussian Naive Bayes |
|
Nearest Neighbors |
A basic example showing some of these classifier models can be found in Classifier comparison.