 # Linear Regression In Machine Learning: A Simple Overview In 4 Points

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## Introduction

Linear regression in machine learning is a widely used algorithm in machine learning and statistics. Before we get into the details of linear regression you need to know why we have to look at this particular algorithm.

Machine learning is the field of predictive modelling and this is mostly concerned with minimizing errors on models or making the best and the most accurate prediction. In the study of machine learning, we borrow, steal and reuse the algorithm from different fields that includes statistics as well.

Linear regression in statistics got developed in this field and is studied in the form of a model to understand the relation between the input and the output numerical variable but which is borrowed by machine learning. It is both a statistical as well as a machine learning algorithm.

## 1. Many Names of Linear Regression

When you look into linear regression in machine learning, it can be a bit confusing because linear regression has been studied in various angles, and each angle has brought up a new name for it.

Linear regression is a linear model that takes a linear relationship between the input variable which is x and the output variable that is y. Y is thus calculated as a linear combination of the input variable

When one single input variable x is involved then the method is the simple linear regression. In the case of many input variables, it is the multiple linear regression.

Various methods are used to predict the linear regression equation from the data and the most common of these is the ordinary least squares method. Let us now understand the representation of the linear regression.

## 2. Linear Regression Model Representation

Linear regression in machine learning is a very attractive model and this is because of its easy representation. It is represented in a simple linear equation that combines two sets of data. This is the input variable or x and the solution to it is the output predicted for that set of input values which is y. The input and the output are numeric

The linear equation will assign one small factor to every input value or column, which is the coefficient, and this is represented by the Greek letter Beta. There is one additional coefficient that is added that gives the equation an additional degree of freedom and thus is called the bias or the intercept coefficient.

In the case of more than a single input, this is called the hyperplane. The representation is done in the form of an equation.

In case the coefficient of regression is zero, then the influence of the input variable gets removed, and the prediction that the model makes is a 0.

Now that you understand the representation that is used for the linear regression model let us understand how to predict with the linear regression.

## 3. Making Predictions with Linear Regression

When you are given that the representation is a linear equation, then it makes it easy to predict, which is as simple as solving the equation for a specific input set.

Like in the linear regression example suppose you want to predict weight y from the height x. The linear regression model is

y = B0 B1 * x1

The technique is used to find some good coefficient values and once it is found it can be plugged into varied height values and this can be used to predict weight.

Now that you know how can use the linear regression model to make predictions when you are given a linear regression model here is how to prepare data for the same.

## 4. Preparing Data for Linear Regression

There is a lot of theory on how the data should be structured that lets you make the best use of the Linear regression in machine learning model. These rules mentioned below can be used as a thumb rule when the ordinary least squares regression model is used, which is also the most popular of the linear regression models.

Here is how you can prepare the data in various ways.

• The linear assumption is where it is assumed that the relationship between the input and the output is linear.
• Remove Noise is where the linear regression will assume that the input, as well as the output variables, are not very noisy. This is important for your output variable as you want to remove the outliers.
• Remove Collinearity is the linear regression that will fit over the data when you have the highly correlated input variable.
• Gaussian Distributions where the linear regression will make a reliable prediction.
• Rescale Inputs is when the linear regression will be able to make some reliable predictions if the input variables are rescaled using normalization or standardization.

## Conclusion

When you learn the Linear regression in machine learning model, you are estimating the coefficient values of the condition, which is used in representing the available data. Four techniques are sued to prepare the linear regression. The ordinary least square is the most common method that is used.

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