All classification models explained

When studying machine learning, you may encounter many different types of models and algorithms, and there is a popular branch in supervised learning that is classification models.

Supervised learning has 2 types of models: classification and regression. Well, in this article, we will get to know about the most popular classification models there are out there.

What is the Classification Algorithm?

Classification algorithm belongs to supervised learning. And it is employed to categorise data into several groups. These methods use linear and nonlinear algorithms to partition and categorise the dataset. The output might be categorised or binary.

The output can be be classified such as, Yes or No, 0 or 1, etc.

Types of Classification Algorithms

Classification algorithms use two sorts of models: linear and non-linear models.

I’ve written articles on each of the models listed below, explaining them with examples, so you may read them.

Linear Models

In this type the algorithm uses a straight line to classify between classes.

  • Logistic Regression
  • Support Vector Machines

Non-linear Models

In this type the algorithm uses non-linear(curves) to classify the dataset into classes

  • Decision Tree Classification
  • Random Forest Classification
  • K-Nearest Neighbours
  • Naïve Bayes
  • Kernel SVM

Well, if you like this article you can check out my articles for more interesting articles in the field of artificial intelligence and machine learning.

Conclusion

If you found this article useful please appreciate it by giving claps and follow me for more interesting articles. Well, I have good news for you I would be bringing more articles to explain machine learning concepts and models with codes so leave a comment and tell me how excited are you about this.

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Aviral Bhardwaj

Aviral Bhardwaj

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One of the youngest writer and mentor on AI-ML & Technology.