What is the difference between inductive and deductive learning in machine learning? | Glue Labs

What is the difference between inductive and deductive learning in machine learning? Inductive learning, also known as Concept Learning, is how AI systems attempt to use a generalised rule to carry out observations.

When the output and examples of the function are fed into the AI system, inductive learning attempts to learn the function for new data.

Therefore, inductive learning, or induction, is making an inference based on observation, often of a sample. On the other hand, deductive learning, or deduction, is making an inference based on widely accepted facts or premises.

Now we know what inductive learning is, but where is it used? The Inductive Learning Algorithm (ILA) is an iterative and inductive machine learning algorithm that is used for generating a set of classification rules, which produces rules of the form “IF-THEN” for a set of examples, producing rules at each iteration and appending to the set of rules.

Examples include statistical machine learning algorithms like KNN (K-nearest neighbour) or SVM (Support Vector Machine). Now let’s talk about deductive machine learning.

Deductive machine learning techniques are typically used to add knowledge that is implied by existing knowledge in a deductive manner, which may lead to faster solutions. Examples include machine learning algorithms to deduce reasoning using a decision tree.

Therefore, now we can conclude that deductive learning moves from a generalised statement to a valid conclusion (top-down approach), whereas inductive learning moves from a specific observation to a generalisation (bottom-up approach).