What are regression models in supervised learning
In machine learning we have different types of models and algorithms. As the regression models comes in the field of supervised learning. In this type the model predicts an integer number as output.
In this article we will discuss about what is regression algorithm, types of regressors, and where we can use regression models.
What is the Regression algorithm
The regression algorithm is a part of Supervised Learning. And it uses statistical method to model the relationship. 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.
In the above image we have an height and weight dataset graph. As you know as the heights increases the weight also increases. So we have plot a straight line to correlate our data.
Now image you’re working for a new channel and your task is to predict the temperature for next week. The company channel has provided you with a dataset in that given dataset there are some basic information of past weather conditions like humidity, and etc.
Here you can make a regression model which would help in predicting what would be the temperature for next few days
Types of Regression Algorithms
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Decision Tree Regression
- Random Forest Regression
- Support Vector Regression
Use cases of Regression Algorithms
Regression algorithms can be used in many areas.
- Stock, house price prediction
- Prediction of a numerical value
- Weather forecasting
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