All classification models explained
All classification models explained in supervised learning
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
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Conclusion
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