Supervised learning is a learning in which you train the machine learning algorithm using data that is already labeled. This means that the correct answer is already known for all the training data. After training, it is provided with a new set of unknown data which the supervised learning algorithm analyses, and then it produces a correct outcome based on the labelled training data.
Unsupervised learning is where the algorithm is trained using information, for which the correct label is not known. Here the machine basically has to group together information according to the various patterns, or any correlations without training on any data beforehand.
Regression is a form of a supervised machine learning technique that tries to predict any continuous valued attribute. It analyses the relationship between a target variable (dependent) and its predictor variable (independent). Regression is an important tool for data analysis that can be used for time series modelling, forecasting, and others. Regression data mining techniques are of varied types and help cover a broad spectrum of prediction and impact assumptions that are later useful for curating machine learning datasets.
Regression involves the process of fitting a curve or a straight line on various data points. It is done in such a way that the distances between the curve and the data points come out to be the minimum.
In the world of AI and ML, one of the most popular words is data mining. As the name suggests, it is the process of skimming through a large data pool to recognize the pattern and existing relationships, which can further be implied to solve business problems. There are a hand full methods to perform data mining regression being one of them.
Therefore, in other words, it can be said that regression in data mining is a tool that helps predict numerical values in a given data set, such as predicting temperature, cost or such values. Hence, regression techniques in data mining are widely popular in business settings, most popularly in marketing, trend analysis and varied kinds of financial forecasting.
Though linear and logistic regressions are the most popular types, there are many other types of regression that can be applied depending on their performance on a particular set of data. These different types vary because of the number and type of all dependent variables and also on the kind of regression curve formed.
Regression in data mining can be of various types. Below are the data mining regression methods that are used widely.
Linear Regression forms a relationship between the target (dependent) variable and one or more independent variables using a straight line of best fit.
As the name suggests, linear regression in data mining functions by building a straight line between the target variable and one or more than one independent variable.
It is represented by the equation:
Y = a + b*X + e,
where a is the intercept, b is the slope of the regression line and e is the error. X and Y are the predictor and target variables respectively. When X is made up of more than one variables (or features) it is termed as multiple linear regression.
The best-fit line is achieved using the Least-Squared method. This method minimizes the sum of the squares of the deviations from each of the data points to the regression line. The negative and positive distances do not get cancelled out here as all the deviations are squared.
There are also divisions under linear regression in data mining named simple regression and multiple regression. Simple linear regression is where a singular predictor variable is known. However, in most real-world cases, the number of predictor variables is more than one, which is why multiple Regression data mining is used more than the simple one.
In polynomial regression, the power of the independent variable is more than 1 in the regression equation. Below is an example:
Y = a + b*X^2
In this particular regression, the line of best fit is not a straight line like in Linear Regression. However, it is a curve that is fitted to all the data points.
Implementing polynomial regression can result in over-fitting when you are tempted to reduce your errors by making the curve more complex. Hence, always try to fit the curve by generalizing it to the problem.
Logistic regression is used when the dependent variable is of binary nature (True or False, 0 or 1, success or failure). Here the target value (Y) ranges from 0 to 1 and it is popularly used for classification type problems. Logistic Regression doesn’t require the dependent and independent variables to have a linear relationship, as is the case in Linear Regression.
Not to be confused by the etymology of the regression method, it is not linked to logistics. Rather the name comes from a mathematical technique. The purpose of logistic regression is to measure the impact of multiple variables on given outcomes. Such as the impact of age on social media addiction.
Due to this facility, Logistic regression is widely used in machine learning for binary classification problems. Its ability to turn complex calculations of probability into simple arithmetic problems is commendable and is one of the biggest reasons behind its soaring popularity in business, especially e-commerce.
Ridge Regression is a technique used to analyze multiple regression data that have the problem of multicollinearity. Multicollinearity is the existence of an almost-linear correlation between any two independent variables.
It occurs when the least squares estimates have a low bias, but they have high variance, so they are very different from the true value. Thus, by adding a degree of bias to the estimated regression value, the standard errors are greatly reduced by implementing ridge regression.
The cost function for ridge regression is given below.
Min(||Y – X(𝛉)||2 + λ||𝛉||2)
Here λ is the penalty term and its value of it is controlled by an alpha parameter. The higher the value of the alpha parameter is, the bigger the penalty term gets, hence the magnitude of its coefficient gets reduced.
The usual regression equation base that is used for any type of machine learning model is given below.
Y= XB + e
Here, Y is the dependent variable, X represents the independent variable, B is the regression coefficient and e stands for errors
In the case of ridge regression, the assumptions are quite similar to linear regression due to their partial similarities. That are constant variance, linearity and independence. Even so, differing from linear regression, ridge regression does not provide confidence limits.
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The term “LASSO” stands for Least Absolute Shrinkage and Selection Operator.
It is a type of linear regression that uses shrinkage. In this, all the data points are brought down (or shrunk) towards a central point, also called the mean. The lasso procedure is most suited for simple and sparse models that have comparatively fewer parameters. This type of regression is also well-suited for models that suffer from multicollinearity (just like a ridge).
Similar to ridge regression, lasso regression is also useful when the dataset is high on multicollinearity or even when someone wants to automate variable deletion and implement feature selection.
The statistical equation of lasso regression is given below.
D = d12 + d22 + d32 + d42 + d52 + d62 + d72 + d82 + d92
Here d1, d2, d3… are the distance between the actuarial point and the mode line.
There are also some other kinds of regression techniques in data mining that are used in machine learning such as polynomial regression, Stepwise regression and elastic net regression.
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Polynomial regression, also known as multiple linear regression, as the name tells is a regression algorithm that establishes a relationship between dependent and independent variables.
The equation for this regression algorithm is :
y= b0+b1x1+ b2x12+ b2x13+…… bnx1n
It is very similar to a linear model but with some modifications so that a higher level of accuracy can be achieved. Also, the dataset used for this regression is of the non-linear type so that complicated nonlinear functions and datasets can fit into the linear regression model.
The accuracy of output and ability to handle data that is non-linear are reasons for choosing a polynomial regression model over the linear one.
On that note, polynomial regression is often called polynomial linear regression as it depends on the coefficients that are distributed in a linear manner rather than the variables.
Simply put, the elastic net is a regression method that does variable selection and regularization at the same time.
To do so, it uses penalties both from Lasso and Ridge regression models. It can be said that this model is curated by correcting the shortcomings of both models. ElasticNet improves the limitations of Lasso and curates high-dimensional data.
This method implements two types of shrinkage on the coefficients. Therefore, this method is recommended when dimensional data is greater than the number of samples used.
It is a type of regression algorithm that comes in handy when there is uncertainty regarding the predictor variables. The stepwise regression model works by adding or removing individual variables from the model and analyzing their impact on the accuracy.
Even though the method is very popular, it is recommended quite less due to varied reasons.
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Regression analysis basically allows you to compare the effects of different kinds of feature variables measured on a wide range of scales. Such as the prediction of house prices based on total area, locality, age, furniture, etc. These results largely benefit the market researchers or data analysts to eliminate any useless features and evaluate the best set of features to build accurate predictive models.
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