In most supervised machine learning algorithm, our main goal is to find out a possible hypothesis from the hypothesis space that could possibly map out the inputs to the proper outputs.

The following figure shows the common method to find out the possible hypothesis from the Hypothesis space:

**Hypothesis Space (H): **

Hypothesis space is the set of all the possible legal hypothesis. This is the set from which the machine learning algorithm would determine the best possible (only one) which would best describe the target function or the outputs.

**Hypothesis (h): **

A hypothesis is a function that best describes the target in supervised machine learning. The hypothesis that an algorithm would come up depends upon the data and also depends upon the restrictions and bias that we have imposed on the data. To better understand the Hypothesis Space and Hypothesis consider the following coordinate that shows the distribution of some data:

Say suppose we have test data for which we have to determine the outputs or results. The test data is as shown below:

We can predict the outcomes by dividing the coordinate as shown below:

So the test data would yield the following result:

But note here that we could have divided the coordinate plane as:

The way in which the coordinate would be divided depends on the data, algorithm and constraints.

Hence, in this example the hypothesis space would be like:

## Recommended Posts:

- Understanding Hypothesis Testing
- The Lottery Ticket Hypothesis
- Understanding Logistic Regression
- ML | Understanding Data Processing
- Understanding Tensor Processing Units
- Understanding Types of Means | Set 1
- Understanding Types of Mean | Set 2
- Understanding different Box Plot with visualization
- Understanding Activation Functions in Depth
- OpenCV | Understanding Brightness in an Image
- Understanding of LSTM Networks
- Understanding BERT - NLP
- Understanding GoogLeNet Model - CNN Architecture
- Analysis required in Natural Language Generation (NLG) and Understanding (NLU)
- Understanding PEAS in Artificial Intelligence
- Basic Understanding of CURE Algorithm
- Basic Understanding of Bayesian Belief Networks
- Basic understanding of Jarvis-Patrick Clustering Algorithm
- Understanding Auxiliary Classifier : GAN
- Understanding of OpenSeq2Seq

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.