24 Sep Inductive vs Deductive Machine Learning
Machine Learning includes two types of learning: Inductive and Deductive. Let us understand them one by one and also see their differences.
Inductive Learning
Under this type of learning, the learner observes instances and discovers rules based on defined principles to draw conclusions.
First, the model learns patterns from the training data, then these patterns are applied to make predictions on new, unseen data. Such a learning approach is commonly used in supervised learning scenarios.
Example: Letting a kid know to stay away from a knife by displaying a video wherein using a knife can cause an injury.
Examples of Inductive Learning Algorithms
- Decision Trees
- Neural Networks
- K-Nearest Neighbors (KNN)
Advantages of Inductive Learning
- Data: Handles complex data.
- Pattern Recognition: Identifies hidden patterns and relationships in data easily.
Disadvantages of Inductive Learning
- Overfitting: Inductive learning may overfit the training data. This can lead to poor generalization of new data.
Deductive Learning
Under this type of learning, the conclusion is drawn from experiences. The deductive approach applies known principles to derive specific outcomes.
Example: Let the kid play with a knife. If he/ she suffers an injury, the next time, the sense of cautiousness would be there that the knife is dangerous.
Examples of Deductive Learning Algorithms
- Rule-based systems: Uses a set of predefined rules to make decisions.
- Expert systems: Applies domain-specific knowledge to solve problems.
Advantages of Deductive Learning
- Transparency: The reasoning process is transparent
- Conclusions: The conclusions are logically derived from the given rules, making sure they are probably true.
Disadvantages of Deductive Learning
- Less flexible: Less flexible in handling new or unseen data that doesn’t fit the predefined rules.
Let us see the differences between Inductive and Deductive learning:
| Inductive Learning | Deductive Learning | |
|---|---|---|
| What | Under this type of learning, the learner observes instances and discover rules based on defined principles to draw conclusion. | Under this type of learning, the conclusion is drawn from experiences. The deductive approach applies known principles to derive specific outcomes. |
| Learning Approach | Bottom-up i.e. from specific examples to general rules | Top-down: i.e. from general rules to specific instances |
| Data | Requires a large amount of data to identify patterns | Requires predefined rules or theories |
| Flexibility | More flexible. It can easily handle new and unseen data. | Less flexible. It relies on existing rules |
| Certainty of the outcome | Produces probabilistic conclusions | Produces certain conclusions based on rules |
| Advantages | Effective at identifying hidden patterns. Can handle complex data. | Transparent reasoning process. The conclusions are logically derived. |
| Disadvantages | May overfit to training data. Requires large datasets | Less adaptable to new data. Rigid in application |
| Examples | Decision Trees, K-Nearest Neighbors (KNN), etc. | Rule-Based Systems, Expert Systems |
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