Classification models
What are classification models in supervised learning
In machine learning we have different types of models and algorithms. As the classification models comes in the field of supervised learning. In this type the model classify the data into classes.
In this article we will discuss about what is classification algorithm, types of classifier, and where we can use classification models.
What is the Classification Algorithm?
The Classification algorithm come in the field of Supervised Learning. In classification models we classify our dataset into many classes and these algorithms uses are a linear or a nonlinear approach to differentiate between the data into classes.
As in the above image we have a dataset of black, white and grey dots. The classification model has made 3 lines to separate the dataset into classes i.e. class 1, class 2, and class 3. The output can either binary or categorical.
Types of classifier
There are mainly 2 types of classifiers
Binary Classifier
In this type of classification the output predicted by the model has only 2 possibilities like yes or no
Multi-class Classifier
In this type of classification technique the model can predict more than 2 outputs like colours i.e blue, red, black
Types of Classification Algorithms
Further in these two classes we have different type of model that uses
Linear Models
In this type the model uses a line to classify between dataset.
- Logistic Regression
- Support Vector Machine (SVM)
Non-linear Models
In this type the model uses a curve to classify between dataset.
- Decision Tree Classification
- Random Forest Classification
- K-Nearest Neighbours
- Naïve Bayes
- Kernel SVM
Use cases of Classification Algorithms
We can use classification models in many areas.
- Targeted marketing
- Spam filtering
- Image classification
Example
Now let’s say you are working for an insurance company and your target is to sell the insurance to maximum people. now the bank has provided you a dataset of a past employe. In that given dataset there are some basic information of customers like age, income and etc. and all the people who has bought the insurance in the past.
Here you can make a classification model which would help in predicting what kind of person would buy insurance. so you can spend your limited time to sell the insurance to maximum people.
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Conclusion
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