01 Oct Supervised vs Unsupervised vs Reinforcement
Let us see the differences between Supervised, Unsupervised, and Reinforcement Learning in Machine Learning. Before that, let us quickly revisit what we learned in the previous lessons.
In supervised learning, the machine is enabled to predict based on labeled data fed to it. In layman’s terms, we can easily say that the models are trained on labeled examples and make predictions on unlabelled examples.
In unsupervised learning, the model identifies patterns and relationships in the input without any prior knowledge about the data, unlike supervised learning. That means, their no training data.
In reinforcement learning, the Machine Learning model learns based on the rewards it received (or penalties) for its previous actions.
Let us now see the differences:
| Supervised Learning | Unsupervised Learning | Reinforcement Learning | |
|---|---|---|---|
| What | Learning with labeled data | Learning with unlabeled data | Learning through rewards and penalties |
| Training Data | Labeled (with input-output pairs) | Unlabeled (no predefined labels) | Interacting with an environment |
| Aim | Predict outcomes based on input data | Find hidden patterns or intrinsic structures in data | Maximize cumulative reward |
| Feedback | Direct feedback on predictions | No direct feedback, patterns discovered by the model | Feedback through rewards and penalties |
| Data Requirement | Needs a large amount of labeled data | Can work with large volumes of unlabeled data | Requires a lot of interaction with the environment |
| Process | Training with explicit pairs of input and output | Learning by discovering patterns from data itself | Learning by trial and error |
| Level of ease | Easier once data is labeled | Complex due to the need to identify underlying patterns | Can be very complex due to dynamic environment |
| Examples/ Types | Classification, regression | Clustering, dimensionality reduction, association | Game playing, robotics, autonomous vehicles |
| Algorithms | Support Vector Machine, Decision Trees, Linear Regression | K-means clustering, Principal Component Analysis (PCA) | Deep Q Networks (DQN), Q-learning, Policy Gradients |
| Applications/ Use case | Email spam detection, house price prediction | Customer segmentation, market basket analysis | Self-driving cars, robotic control, games, etc. |
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