What is the difference between supervised, unsupervised, and reinforcement learning?
# Supervised Learning
In a supervised learning model, the algorithm learns on a labeled dataset, to generate reasonable predictions for the response to new data. Ex:
- Regression
- Classification
# Unsupervised Learning
An unsupervised learning model in contrast provides unlabelled data that the algorithm tries to make sense of by extracting features, co-occurrence and underlying patterns on its own. Ex:
- Clustering
- Anomaly detection
- Associations
- Autoencoders
# Reinforcement Learning
Reinforcement learning is less supervised and depends on the learning agent in determining the output solutions by arriving at different possible ways to achieve the best possible solution.
Source: iNeuronai