Introduction
There is no doubt that machine learning has become one of the most significant trending technologies, and it is used widely across various applications.
Due to the frequent use of machine learning in diverse fields, more people are moving toward specializing in it.
Regression analysis is one of the most fundamental types of machine learning algorithms. Because most practitioners begin their machine learning journey with Supervised Learning, regression analysis is typically learned during the initial phase.
Now, let’s try to understand what regression analysis actually is.
What is Regression Analysis?
Regression Analysis can be understood as a set of statistical processes or approaches used to estimate the relationships between a dependent variable and one or more independent variables. In layman's terms, it is used to understand how small changes in independent variables affect the dependent variable.
In regression algorithms, the target variable (the predicted value) is a continuous numeric variable—for example, the sales and income of an entity, house prices, or student test scores.
It falls under the category of supervised machine learning. Another main type of supervised machine learning is known as Classification. While Logistic Regression is often considered a classification technique, we are listing it here within the regression algorithms due to its underlying structure.
Mainly, the three major uses of regression analysis are the determination of:
- Strength of predictors
- Forecasting effects
- Trend forecasting
Types of Regression Algorithms
There are six main types of regression algorithms:
- Linear Regression
- Multiple Regression
- Polynomial Regression
- Ridge Regression
- Lasso Regression
- Elastic Net Regression
We will learn about each of them one by one.
So, let’s get started!
Linear Regression
Linear regression algorithms are used when there is only one dependent variable and one independent variable, denoted by y and x, respectively. These algorithms examine two factors:
- How closely are x and y related?
The algorithm provides a number between -1 and 1, which indicates the correlation between the variables.
Where:
0 indicates no relation
1 indicates a perfect positive correlation
-1 indicates a perfect negative correlation
- Prediction
When the relationship between x and y is established, the model is used to predict results for unknown x variables. This is done by fitting a linear relationship, which is represented as:
y = mx + c
x = independent variable
y = dependent variable
m = slope (weight)
c = intercept
Multiple Linear Regression
Multiple Linear Regression is a statistical technique used to predict the outcome of a dependent variable that relies on more than one independent variable. While simple linear regression only runs when there is one independent variable, multiple linear regression is used when there are several.
Polynomial Regression
Linear regression works only for straight-line functions (first-degree equations), whereas in polynomial regression, the independent variable contains polynomial functions. Therefore, the Polynomial Regression algorithm is applied when the relationship between variables is non-linear.
Although polynomial regression is used to fit non-linear datasets, it is most widely used for curvilinear data patterns.
Ridge Regression
Ridge Regression is used when a dataset faces the problem of multicollinearity. The goal of this technique is to reduce variance by adding a degree of bias to the regression estimates, identifying a new line that generalizes better. In brief, it is used to reduce standard errors.
Here, bias refers to the error introduced by over-simplistic assumptions in the learning algorithm:
- Model underfitting: The model is too simple to capture the data's structure.
- Low predictive accuracy: It becomes difficult for the model to make precise predictions.
Variance refers to the error caused by excessive complexity in the learning algorithm, leading to overfitting.
Lasso Regression
Lasso Regression is similar to ridge regression, but it also includes feature selection. It can set the coefficient values for unimportant features to zero, effectively removing them from the model.
LASSO (Least Absolute Shrinkage and Selection Operator) Regression is used to simplify models by reducing the number of variables. It is broadly used in regression analysis to perform both regularization and variable selection.
Elastic Net Regression
Elastic Net Regression is a combination of Lasso and Ridge regression. When it is difficult to choose between Lasso and Ridge, practitioners often adopt Elastic Net as a hybrid of both techniques. Elastic Net is particularly useful when the number of predictors exceeds the number of observations.
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