References
https://techvidvan.com/tutorials/types-of-machine-learning/
https://kimola.com/cognitive/resources/how-to-use-machine-learning-in-qualitative-research
Inductive reasoning involves starting from specific premises and forming a general conclusion, while deductive reasoning involves using general premises to form a specific conclusion.
Conclusions reached via deductive reasoning cannot be incorrect if the premises are true. That’s because the conclusion doesn’t contain information that’s not in the premises. Unlike deductive reasoning, a conclusion reached via inductive reasoning goes beyond the information contained within the premises—it’s a generalization, and generalizations aren’t always accurate.
-
Inductive learning
Inductive learning involves the creation of a generalized rule for all the data given to the algorithm. In this, we have data as input and the results as output; we have to find the relation between the inputs and outputs.
This can be very complex depending on the data. But, it is an effective method used in ML and used in various fields of ML like facial recognition technology, disease cure, and diagnosis, etc. It uses a bottom-up approach.
This algorithm is crucial as it gives us a relation between data that has a use for future references. It’s used when human expertise doesn’t work when the outputs are varying etc. In short, we can say that in inductive learning, we generalize conclusions from given facts. For example,
a. Apple is a fruit.
b. Apple tastes sweet.
Conclusion: All fruits taste sweet. -
Deductive learning
Just like Inductive reasoning, deductive learning or reasoning is another form of reasoning. In reality, the reasoning is an AI concept and both inductive and deductive learning are part of it.
Unlike inductive learning, which is based on the generalization of specific facts, deductive learning uses the already available facts and information in order to give a valid conclusion. It uses a top-down approach.
The one major thing to note is that in deductive learning, the results are certain i.e, it is either yes or no. Whereas it’s probability-based on inductive learning i.e, it can range from strong to weak.
a. All carnivores eat meat.
b. Lion is a carnivore.
Conclusion: – Lion eats meat.
