Comparison of Supervised, Unsupervised, Semi-Supervised and Reinforcement

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Supervised, unsupervised learning, semi-supervised and reinforced learning are 4 fundamental approaches of machine learning:

  • Supervised Learning Builds a model based labelled data.
  • Unsupervised Learning Builds a model based on a unlabelled data.
  • Semi-Supervised Learning Builds a model based on a mix of labelled and unlabelled data. This sits between supervised and unsupervised learning approaches.
  • Reinforcement Learning This is a feedback-based learning method, based on a system of rewards and punishments for correct and incorrect actions respectively. The aim is for the “learning agent” to receive maximum reward and hence improve its performance.

Overview comparison between these methods

Category Supervised Unsupervised Semi-supervised Reinforcement
Input data All data is labelled All data is unlabelled Partially labelled No predefined data
Training? External supervision No supervision ?? No supervision
Use Calculate outcomes Discover underlying patterns ?? Learn a series of outcomes
Computational complexity Simple Complex Depends ??
Accuracy Higher Lesser Lesser ??

Example algorithms under each approach

Below is a basic comparison table of the different approaches with a few example algorithms:

Supervised Unsupervised Semi-supervised Reinforcement
Decision trees K-means Generative adversial networks Q-learning
Support Vector Machine A-priori Self-trained Naïve Bayes classifier Model-based value estimation
Linear regression Hierarchical clustering Low-density separation Policy optimization
Logistic regression K Nearest Neighbours Laplacian regulation State-Action-Reward-State-Action
Naive Bayes Principal Component Analysis Heuristic approaches Deep Q Network

Example uses of each approach

Below is a basic comparison table of the different approaches with a few example uses:

Supervised Unsupervised Semi-supervised Reinforcement
Image recognition Customer segmentation Text document classifier Playing games e.g. chess game
Market prediction e.g. house prices Anomaly e.g. fraud detection Speech analysis Self-driving cars

Challenges using each approach

Supervised Unsupervised Semi-supervised Reinforcement
Pre-processing of data may be time consuming More time required by user e.g. for interpretation Complex iterative process Choosing reward structures wisely
Cannot give “unkown” information as per unsupervised learning May result in less accurate predictions compared to supervised learning Not as accurate as supervised learning Fast learing given small samples
Cannot handle “complex tasks” Computationally more complex that supervised learning Cannot handle more “complex tasks” Not preferable for learning “simple problems”

Further reading / Links

IBM article