Classification Algorithm in Machine Learning

Classification is defined as the process of recognition, understanding, and grouping of objects and ideas into preset categories i.e. “sub-populations.” With the help of these pre-categorized training datasets, classification in machine learning programs leverage a wide range of algorithms to classify future datasets into respective and relevant categories

Classification algorithms used in machine learning utilize input training data for the purpose of predicting the likelihood or probability that the data that follows will fall into one of the predetermined categories. One of the most common applications of classification is for filtering emails into “spam” or “non-spam”, as used by today’s top email service providers.

?

Learners in Classification problem:

There are two types of learners.?

? Lazy Learners: It first stores the training dataset before waiting for the test dataset to arrive. When using a lazy learner, the classification is carried out using the training dataset's most appropriate data. Less time is spent on training, but more time is spent on predictions.?

Some of the examples are case-based reasoning and the KNN algorithm.?

? Eager Learners: Before obtaining a test dataset, eager learners build a classification model using a training dataset. They spend more time studying and less time predicting.?

Some of the examples are ANN, naive Bayes, and Decision trees.

There are two different types of Classification Tasks in Machine Learning and they are following –?

? Binary Classification?

? Multi-Class Classification

Binary Classification:

Binary Classification refer to that classification tasks that require 2 class labels. Typically, binary classification tasks involve one class that is the normal state and another class that is the abnormal state. The class for the normal state is assigned the class label 0 and the class with the abnormal state is assigned the class label 1.

e.g.??Email spam detection (spam or not).?

Churn prediction (churn or not).?

Conversion prediction (buy or not).

Multi-Class Classification:

Multi-Class Classification refer to those classification tasks that require more than two class labels. Unlike binary classification, multi-class classification does not have the notion of normal and abnormal outcomes. Instead, examples are classified as belonging to one among a range of known classes.?

Problems that involve predicting a sequence of words, such as text translation models, may also be considered a special type of multi-class classification. Each word in the sequence of words to be predicted involves a multi-class classification where the size of the vocabulary defines the number of possible classes that may be predicted and could be tens or hundreds of thousands of words in size.

e.g.??Face classification.?

?????Plant species classification.?

?????Optical character recognition.?

Types of Classification Algorithms:

Classification Algorithms can be further divided into the Mainly two categories:

o Linear Models

o Logistic Regression

o Support Vector Machines

o Non-linear Models

o K-Nearest Neighbors

o Na?ve Bayes

o Decision Tree Classification

o Random Forest Classification

o Neural Networks