Regression Algorithms Used In Data Mining
Regression algorithms are a subset of machine learning, used to model dependencies and relationships between inputted data and their expected outcomes to anticipate the results of the new data.
Regression algorithms predict the output values based on input features from the data fed in the system. The algorithms build models on the features of training data and using the model to predict value for new data.
They have many uses, most notably in the financial industry, where they are applied to discover new trends and looking at future forecasts.
Here are five of the most used regression algorithms and models used in data mining.
Linear Regression Model
Simple linear regression lets data scientists analyse two separate pieces of data and the relationships between them.
The model assumes a linear relationship exists between input variables and the singular output. This can be calculated from a linear combination of both the input variables.
Examples of linear regression models include predicting the value of houses in the real estate market and analysing road patterns to predict where the highest volume of traffic is.
Simple variable regression implies that there is only a single input variable. Where there are more than one variables, the method is known as multiple linear regression.
Unlike linear regression technique, multiple regression is a broader class of regressions that encompasses linear and nonlinear regressions with multiple explanatory variables.
One business application of the multiple regression algorithm is its use in the insurance industry to decide whether or not a claim is valid and needs to be paid out. This example has many different variables to consider, so a linear regression algorithm wouldn’t be appropriate,
Multivariate Regression Algorithm
This technique is used when there is more than one predictor variable in a multivariate regression model and the model is called a multivariate multiple regression. It’s one of the simplest regression models used by data scientists.
Multivariate regression algorithms are used to predict the response variable for a set of explanatory variables. This regression technique can be implemented efficiently with the help of matrix operations.
These algorithms are used as part of the AI revolution of the medical industry. Doctors require a lot of data collected from their patients, including heart rates and cholesterol levels to external factors such as how much they exercise.
They may want to investigate the relationships between their patient’s activity compared to how much cholesterol they have in the body.
This next data mining regression algorithm is another popular method used in the financial industry, particularly in the credit checking business. This is because logistic regression requires a binary response.
In regression analysis, logistic regression estimates the parameters of a logistic model. More formally, a logistic model is one where the log-odds of the probability of an event is a linear combination of independent or predictor variables.
There are two possible dependent variable values: “0” and “1”. These are used to represent outcomes such as pass/fail or win/lose.
One of the major upsides is of this popular algorithm is that one can include more than one dependent variable which can be continuous or dichotomous. The other major advantage of this supervised machine learning algorithm is that it provides a quantified value to measure the strength of association according to the rest of the variables.
Going back to the credit scoring industry, it can be easy to see how it is used; companies apply logistic regression to see if a customer meets the necessary criteria to be eligible for a loan/credit card/etc.
Lasso (ie Least Absolute Selection Shrinkage Operator) regression algorithms are used to obtain the subset of predictors that minimize prediction error for a quantitative response variable. The algorithm operates by imposing a constraint on the model parameters that causes regression coefficients for some variables to shrink toward a zero.
If the algorithm assigns a coefficient to zero, the variable is no longer used as part of the model. Those that have a non-zero coefficient are then used as part of the response.
Explanatory variables can be either quantitative, categorical or both. Lasso regression analysis is basically a shrinkage and variable selection method to determine which of the predictors are most important.
Lasso regression algorithms have been widely used in financial networks and economics. In finance, the models have used stock market forecasting, such as how it will react to economic updates and predicting where to invest or the stocks and shares to stay away from.
Support Vector Machines
The final regression data mining algorithm is the support vector machine (SVM). This machine learning regression model is a supervised learning model with associated learning algorithms to analyse data used for classification.
Support vector machine algorithms build models that assign new examples to one category or the other, making it a non-probabilistic binary linear classifier.
The model is a representation of the examples as points in space, mapped so that the examples of the separate categories are divided by a clear gap that is as wide as possible. New examples are then mapped into that same space and predicted to belong to a category based on which side of the gap they fall.
In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using the kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.
Support vector machine regression algorithms have found several applications, including recognition systems that can detect whether an image contains a face. If the SVM identifies a face, it produces a box around it. Data visualisation techniques are included within the SVM algorithm.