Regression algorithms are machine learning algorithms and it’s a breed of supervised learning. It is a statistical model. The main objective of supervised learning algorithms is that finding out the relationship between variables and estimation of value for new data. And the estimation value based on independent variables. They can be called regression tasks.
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Why is Regression key for Machine Learning Problems:
Regression is necessary for any machine learning problem that includes real annual sales and real-life applications.
- Time series forecasting
- Trend Analysis
- Weather analysis
- Financial Forecasting
- Marketing Analysis
Most popular Regression Algorithms are linear regression, logistic regression, multivariate regression,
1) Linear regression model:
Simple linear regression allows us to find the expected value of a random variable. regression analysis that determines the intensity of the relationships between the variables that make up the model.
The simple linear regression forecast is an optimal model for demand patterns with a tendency (increasing or decreasing), that is, patterns that show a linearity relationship between demand and time.
Regression Analysis:
The objective of regression analysis is to determine the relationship between a dependent variable and one or more independent variables. To perform this relationship, a functional relationship between the variables must be postulated.
Applications of linear regression forecast:
· Linear regression can be used in business to estimate trends, Market research, and customer survey results analysis
- Linear regression is used in sales, pricing, and promotions of a product
- Linear regression can also be used to predict financial portfolio prediction, salary forecasting, real estate predictions.
2) Lasso Regression:
The abbreviation of “LASSO” stands for Least Absolute Shrinkage and Selection Operator.
Lasso regression uses shrinkage to shrunk towards a variable like the mean.
LASSO (Operator of reduction and selection of absolute minimums) is a regression method that involves penalizing the absolute size of the regression coefficients.
By penalizing (or equivalently restricting the sum of the absolute values of the estimates) you end up in a situation where some of the parameter estimates may be exactly zero. The higher the penalty applied, the additional estimates are reduced to zero.
This is convenient when we want an automatic selection of variables, or when it comes to highly correlated predictors, where the standard regression will generally have regression coefficients that are “too large”.
Applications of Lasso Regression:
- Lasso regression can be used in financial networks and economics.
- Lasso regression can also be used to perform stress test platforms to predict stress scenarios
- Lasso based regression models are used to find out risk Skelton for enterprises.
Also Read: Keras vs TensorFlow – Know the Difference
3) Logistic Regression:
The Simple Logistic Regression, developed by David Cox in 1958, is a regression method that allows estimating the probability of a binary qualitative variable based on a quantitative variable. One of the main applications of logistic regression is that of binary classification, in which observations are classified in one group or another depending on the value taken by the variable used as a predictor.
The logistic regression technique is the most commonly used technique to detect fraud. Some of the enterprises are applying this technique to find out the fraud like credit card scoring and clinical trials.
Advantages of logistic regression:
- It is a very effective technique, it does not require too many computational resources.
- it outputs well-measured predicted probabilities.
- Logistic regression can also be used to measure the performance of the complex algorithms.
Applications:
- Logistic regression can be used to predict house values in the market.
- We can also use to predict the customer values in the insurance sector
4) Support Vector Machines:
Support vector machines (SVM) are a set of supervised learning algorithms developed by Vladimir Vapnik and his team at AT&T laboratories.
As in most supervised classification methods, the input data are viewed as a p-dimensional vector.
The SVM looks for a hyperplane that optimally separates the points of one class from that of another, which eventually have been able to be previously projected to a space of higher dimensionality.
Models based on SVMs are closely related to neural networks. Using a kernel function, they are an alternative training method for polynomial classifiers, radial base functions, and multilayer perceptron.
Applications:
Support vector machine algorithms can be found in oil gas industries to create 2D and 3D models as a representation of the subsoils.
5) Multi-Variate Regression Algorithm:
A multivariate regression technique is used, when the user wants to predict more than one variable is called a multivariate regression model. And it is one of the efficient supervised algorithms. We can predict the response variable for a set of explanatory variables.
This regression technique is often enforced expeditiously with the assistance of matrix operations and in Python, it is often enforced via the “numpy” library that contains definitions and operations for matrix objects.
Applications:
Multivariate regression can be used in decision making to find out factors that are effecting profits and mostly application of multivariate regression can be seen in the retail industry.
6) Multiple Regression Algorithm:
Most of the enterprises are using multiple regression applications to find out product pricing and real estate pricing and market behaviour.
Unlike regression technique, multiple correlations, maybe a broader category of regressions that encompasses linear and nonlinear regressions with multiple instructive variables.
Applications:
- Multiple regression can be used in behaviour analysis of products and industry.
- Some of the enterprises are using multiple regression to perform social science research.
1 comment
Good article!