Summary Linear regression - all you need to know - beginner to advanced guide
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Course
GATE
Institution
GATE
This document gives you a in-depth knowledge about linear regression and applications of it...it is a beginner friendly and easy to read documentation which takes you from beginner to advanced :)
Machine learning is a subfield of artificial intelligence that focuses on creating statistical models and
algorithms that can learn from data and predict the future. Another sort of machine learning method is
linear regression, which learns from labelled datasets and converts data points into the best-performing
linear functions that may be used to predict outcomes on new datasets.
We should first understand what supervised machine learning algorithms are. The algorithm learns in
this sort of machine learning from labelled data. Datasets with known target values are those that have
been labelled. Two types of supervised learning exist:
Classification: Based on the independent input variable, it predicts the class of the dataset. Categorical
or discrete values are known as classes. Like whether the animal depicted is a dog or a cat?
Regression: Based on the independent input variable, it forecasts the continuous output variables. Like
the estimation of property prices depending on several factors, such as the age of the house, its distance
from the main road, its location, and its neighbourhood.
Here, we'll talk about linear regression, which is among the most basic types of regression.
Linear Regression:
A supervised machine learning approach known as “linear regression” determines the linear relationship
between a dependent variable and one or more independent features. Univariate linear regression
occurs when there is only one independent feature; multivariate linear regression occurs when there
are multiple independent features. The algorithm’s objective is to identify the optimum linear equation
that, given the independent variables, can forecast the value of the dependent variable. The relationship
between the dependent and independent variables is shown by the equation as a straight line. The
slope of the line shows how much the dependent variable changes when the independent variable(s)
are changed by a unit.
Finance, economics, and psychology are just a few of the disciplines that employ linear regression to
analyse and forecast the behaviour of a given variable. For instance, linear regression can be used in the
field of finance to comprehend the connection between a company’s stock price and its earnings or to
forecast the value of a currency based on its historical performance.
Regression is one of the most significant supervised learning tasks. In regression, a series of records with
X and Y values are present, and these values are use
, d to train a function that may be used to predict Y from an unknown X. We need a function that
forecasts continuous Y in the case of regression given X as independent features since we need to find
the value of Y in regression.
Here, X is referred to as an independent variable as well as Y’s predictor, while Y is referred to as the
dependent or target variable. Regression can be done using a wide variety of modules or functions. The
simplest sort of function is a linear function. Here, X could be a single characteristic or a collection of
features that together indicate the issue.
The task of predicting the value of a dependent variable (y) based on an available independent variable
(x) is carried out via linear regression. Consequently, the term “linear regression” was coined. In the
diagram above, a person’s pay is represented by Y, the figure’s output, while their work history is
represented by X, the input. The regression line fits our model the best.
Assumption for Linear Regression Model:
There are a few requirements that linear regression must meet in order to be an effective and
dependable tool for understanding and forecasting the behaviour of a variable.
Linearity: There is a linear relationship between the independent and dependent variables. This suggests
a linear relationship between changes in the independent variable(s) and those in the dependent
variable.
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