The inductive machine learning involves the process of learning by examples, where a system, from a set of observed instances tries to induce a general rule.
Inductive machine learning is a type of machine learning paradigm where the system learns patterns and generalizes from examples in order to make predictions or decisions on unseen data. In this approach, the model is trained on a dataset containing input-output pairs, and it aims to infer the underlying rules or patterns that govern the relationship between the inputs and outputs.
The key characteristics of inductive machine learning include:
- Generalization: The primary goal is to generalize from the training data to make accurate predictions or decisions on new, unseen data points.
- Inductive Bias: It refers to the assumptions or constraints imposed on the learning algorithm, which guide it to prefer one hypothesis over another when multiple hypotheses are consistent with the observed data.
- Learning from Examples: Inductive learning algorithms typically learn by observing a set of example input-output pairs, without being explicitly programmed with predefined rules.
- Probabilistic Inference: Many inductive learning algorithms utilize probabilistic models to estimate the likelihood of different hypotheses given the observed data.
- Evaluation and Testing: The performance of an inductive learning system is evaluated by testing it on a separate dataset not used during training, to assess its generalization ability.
Overall, inductive machine learning is concerned with learning general principles from specific instances, enabling systems to make accurate predictions or decisions on new, unseen data.