Classification Algorithm in Machine Learning
As we know, the Supervised Machine Learning algorithm can be broadly classified into Regression and Classification Algorithms. In Regression algorithms, we have predicted the output for continuous values, but to predict the categorical values, we need Classification algorithms.
What is the Classification Algorithm?
The Classification algorithm is a Supervised Learning technique that is used to identify the category of new observations on the basis of training data. In Classification, a program learns from the given dataset or observations and then classifies new observation into a number of classes or groups. Such as, Yes or No, 0 or 1, Spam or Not Spam, cat or dog, etc. Classes can be called as targets/labels or categories.
Unlike regression, the output variable of Classification is a category, not a value, such as "Green or Blue", "fruit or animal", etc. Since the Classification algorithm is a Supervised learning technique, hence it takes labeled input data, which means it contains input with the corresponding output.
In classification algorithm, a discrete output function(y) is mapped to input variable(x).
The best example of an ML classification algorithm is Email Spam Detector.
The main goal of the Classification algorithm is to identify the category of a given dataset, and these algorithms are mainly used to predict the output for the categorical data.
Classification algorithms can be better understood using the below diagram. In the below diagram, there are two classes, class A and Class B. These classes have features that are similar to each other and dissimilar to other classes.
The algorithm which implements the classification on a dataset is known as a classifier. There are two types of Classifications:
Learners in Classification Problems:
In the classification problems, there are two types of learners:
Types of ML Classification Algorithms:
Classification Algorithms can be further divided into the Mainly two category:
Note: We will learn the above algorithms in later chapters.
Evaluating a Classification model:
Once our model is completed, it is necessary to evaluate its performance; either it is a Classification or Regression model. So for evaluating a Classification model, we have the following ways:
1. Log Loss or Cross-Entropy Loss:
Where y= Actual output, p= predicted output.
2. Confusion Matrix:
3. AUC-ROC curve:
Use cases of Classification Algorithms
Classification algorithms can be used in different places. Below are some popular use cases of Classification Algorithms:
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