Regression in data mining
Regression is a data mining technique used to predict numerical values within a dataset. For instance, regression can be applied to forecast the cost of a product or service, or other variables. It is widely used across industries for analyzing business and marketing behavior, identifying trends, and making financial forecasts.
Regression is a type of supervised machine learning technique used to predict continuous values. It helps businesses analyze the relationship between a target variable and predictor variables. This tool is especially useful for tasks like financial forecasting and time series modeling.
In regression, a straight line or curve is fitted to a set of data points in a way that minimizes the distance between the points and the line or curve.
The most common types of regression are linear and logistic regression. However, there are other types of regression that can be used depending on how well they perform with specific datasets.
Linear Regression
Linear regression is a method that establishes a relationship between a target variable and one or more independent variables using a straight line. The equation for linear regression is:
B= a + b*A + e
Where:
- a is the intercept
- b is the slope of the regression line
- e represents the error
- A and B are the predictor and target variables, respectively.
If A consists of more than one variable, it’s called multiple linear regression.
In linear regression, the best-fit line is determined using the least squares method, which minimizes the sum of the squared deviations from each data point to the regression line. The method ensures that the positive and negative deviations do not cancel each other out since all deviations are squared.
Polynomial Regression
If the power of the independent variable is greater than 1 in the regression equation, it’s called a polynomial regression. In this type of regression, the best-fit line isn't a straight line, but a curve that fits all the data points.
For example, the equation could be:
Y = a + b * x²
In polynomial regression, applying linear regression techniques can lead to overfitting, where the curve becomes too complex in an attempt to minimize errors. To avoid this, it's important to fit the curve in a way that generalizes well to the problem, instead of making it too specific to the data points.
Logistic Regression
When the dependent variable is binary, meaning it has two possible outcomes like 0 and 1, true or false, or success or failure, logistic regression is used. In this case, the target value (Y) falls between 0 and 1 and is typically used for classification problems. Unlike linear regression, logistic regression doesn't require the independent and dependent variables to have a linear relationship.
Ridge Regression
Ridge Regression is a technique used to analyze regression data that faces the problem of multicollinearity, which occurs when two independent variables are highly correlated.
In regular regression, the least square estimates may be biased with high variance, making them significantly different from the real values. Ridge regression helps address this issue by adding some bias to the estimated regression values. This reduces errors and makes the model more reliable by controlling for multicollinearity.
Lasso Regression
LASSO stands for Least Absolute Shrinkage and Selection Operator. It is a type of linear regression that uses shrinkage. In Lasso regression, all the data points are pulled towards a central point, or the mean. The Lasso method is best suited for creating simple and sparse models with fewer parameters compared to other types of regression. It is particularly useful for models that suffer from multicollinearity, helping to reduce the impact of correlated variables.
