Types of Supervised Learning
Introduction to supervised learning in machine learning
Well, if you’re new to the subject of machine learning, you’ve definitely heard of various kinds of learning. If not, don’t worry, in this article, I’ll explain what Supervised learning is.
What is supervised learning?
Supervised learning involves learning a function that maps an input to an output based on example input-output pairs. by the definition supervised learning means this.
Types of models in supervised learning
Regression models
Regression models are continuous and they built a correlation with the dataset and the model try to use a mathematical function for eg it can use either a straight line or a curve to fit the dataset and the output in these types of model can be a number as you can see in the image below.
Types of algorithms used
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Decision Tree Regression
- Random Forest Regression
- Support Vector Regression
Classification models
On the other hand, we have classification models in classification models we categorize into many classes with the help of algorithms the algorithms it uses are a straight line or curve to differentiate between the data into classes the output can be binary or categorical as you can see in the image below.
Types of algorithms used
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
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Conclusion
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