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UNIT-9
CLASSIFICATION &
CLUSTERING
Learning Outcomes
• Understanding Classification in AI/ML • Types of Classification Algorithms
• Confusion Matrix—Evaluating a Classification Model • False Positive or False Negative in Medical Science
• Clustering • K-Means Clustering
• K-Means Generalization • Why is Clustering Unsupervised?
While developing Machine Learning (ML) models, you could manually make a list of features, identify best suited algorithm,
and improve the model parameters in order to have complete control over the model design and recognize all the thought that
went into creating it. However, this approach requires deep knowledge of many Machine Learning algorithms. Classification
is one of machine learning algorithms.
In this unit, you will learn about classification in Artificial Intelligence (AI), classification as a type of supervised learning,
input and output of a classification model. Next, you will learn about to differentiate between the regression problem with
classification problem. Further you will learn how to use the confusion matrix. At the end of the chapter, you will learn about
clustering and its types, and understand K-means clustering algorithm.
Understanding Classification in AI/ML
We use classification every day—classifying vegetables as ‘good to eat’ or ‘rotten’, classifying cats as per their breed or
even classifying files as important or not important.
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