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AI Models
There are mainly three types of AI models: Regression, Classification and Clustering. Let us learn about them in
detail.
Regression
Regression is an example of rule-based AI models. This is a type of Rule-
Based AI model. In regression, the algorithm generates a mapping
function from the given data, as shown by the solid line in the given
graph. The blue dots shown in the graph are the data values and the
solid line here represents the mapping done for them. With the help of
this mapping function, we can predict the future data. It works with
continuous data.
Classification
Classification is another rule-based AI model. It is a systematic
grouping of observations in categories, something like categorising
plants, animals in different taxonomies by biologists. In classification,
you teach the machine to perform with labelled data. Testing data is
then classified as one of the labels of the training dataset. The
algorithm is able to determine which set a given data point belongs
to by means of a classification function represented by the dotted
line. The model classifies datasets according to the rules given to it.
Clustering
Clustering is a machine learning approach where the machine partitions
the dataset into different clusters or categories based on machine-
generated algorithms. The data fed to such a model is usually unlabelled
or random and thus the developer feeds in the data directly into the
machine and instructs it to build its own algorithm. The machine then
forms a pattern or cluster based on training data and groups those that
follow the same pattern. The best clustering is the one that minimizes the error. Clustering works on discrete dataset.
Task
1. Define classification and its significance in AI.
2. Explain the use of labeled data in classification models.
Neural Networks 257

