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What Makes it Different?
Reinforcement Learning is unique because of its ability to handle situations where traditional methods like
supervised or unsupervised learning may fall short. In these approaches, you typically need a clear understanding
of the data and the problem you're solving. However, real-world scenarios often involve complex and dynamic
environments that are not fully understood, and data might be lacking in certain situations. Additionally, the
environment may change over time, requiring the system to adapt. Reinforcement Learning stands out because it
doesn't rely on extensive pre-existing knowledge or large datasets, allowing it to learn from interactions with the
environment and adapt to unforeseen circumstances, making it highly effective in dynamic & uncertain settings.
Summary of ML Models
Family of ML Models
Supervised Unsupervised Reinforcement
Learning Learning Learning
Determine
Discover New Learn by
relationships
Patterns Rewarding Actions
through training
Difference between Supervised Learning, Unsupervised Learning and Reinforcement Learning is explained as
follows:
Aspect Supervised Learning Unsupervised Learning Reinforcement Learning
Nature of Labelled data with input- Unlabelled data; no No labelled data; the model learns
Data output pairs. predefined output labels. through interaction with the
environment and receives rewards or
penalties for actions.
Learning Learns a direct mapping Learns patterns, clusters, or Learns to take actions that maximise
Process between input and structure in data without long-term rewards based on trial and
output. predefined labels. error.
Feedback Instant feedback based No explicit feedback, learns Feedback is delayed and occurs
on the correct label patterns or relationships. based on the outcomes of actions.
Sub-categories of Supervised Learning Models
The Supervised Learning is further categorised as: Classification and Regression. Let us learn about these in
detail.
Classification
Classification is a rule-based AI model. It is a systematic grouping of observations in classes, something like
categorising plants, animals in different categories by biologists. In classification you teach the machine to perform
with labelled data. Testing data is then classified as one of the labels of the training dataset.
The algorithm is able to determine to which set a given data point belongs to, by means of a classification function
represented by the dotted line. The model classifies datasets according to the rules given to it.
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