Understand Linear Regression in Machine learning
Table of Contents
- jaro education
- 5, May 2024
- 10:00 am
Machine learning, a vital part of artificial intelligence, involves creating algorithms and statistical models to learn from data and make predictions. One such algorithm is linear regression, which falls under supervised machine learning. In supervised learning, algorithms learn from labeled datasets, mapping data points to optimized linear functions for prediction on new datasets.
To grasp linear regression fully, it’s essential to understand supervised machine learning. In this type of learning, algorithms learn from labeled data, where each data point has a known target value. Supervised learning includes two key types, classifications and regression.
Classification involves predicting the category or class of a dataset using independent input variables. This typically deals with categorical or discrete values, such as determining whether an image depicts a cat or a dog. Regression, on the other hand, focuses on forecasting continuous output variables based on independent input variables. For instance, it can predict house prices by considering various parameters such as house age, distance from the main road, location, and area.
From understanding simple and multiple linear regression to unveiling its significance, limitations, and practical use cases, you will get a comprehensive understanding of linear regression.
What is Linear Regression?
Linear regression is a frequently utilized method for predictive analysis that clearly defines the relationship between continuous variables. It demonstrates the linear correlation between the independent variable (on the X-axis) and the dependent variable (on the Y-axis), thus the term linear regression. Simple linear regression is defined as having only one input variable (x). Multiple linear regression, on the other hand, is used when several input variables are involved. Essentially, the linear regression model provides a straight line with a predetermined slope that depicts the connection between variables.
Linear regression serves as a predictive instrument and also lays the groundwork for numerous sophisticated models. Some of the methods, such as regularization and support vector machines, are influenced by linear regression. Moreover, linear regression plays a pivotal role in assumption testing, allowing researchers to verify crucial assumptions regarding the dataset.
Type of Linear Regression
As discussed above there are two main types of Linear Regression
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