Machine Learning allows you to use the dataset available to understand a specific function that maps inputs to outputs in the best possible way. This problem is known as function approximation. In this, you need to use an approximation for the unknown target function that maps in the best manner all the plausible observations based on the given problem. Hypothesis in Machine learning is a model that helps in approximating the target function and performing the necessary input-to-output mappings. The choice and configuration of algorithms allows you to define the space of plausible hypotheses that may be represented by the model.
In the hypothesis, lowercase h (h) is used for a specific hypothesis, while uppercase h (H) is used for the hypothesis space that is being searched. Let’s briefly understand these notations:
- Hypothesis (h): A hypothesis is a specific model that helps in mapping inputs to outputs, which can further be used for evaluation and prediction.
- Hypothesis set (H): Hypothesis set consists of a space of hypotheses that can be used to map inputs to outputs, which can be searched. The general constraints include the choice of problem framing, the model, and the model configuration.