Regression Method in Data Mining Simplified 101

A huge data set is used for a variety of applications. Data mining is the process of extracting valuable information from massive amounts of data. You may use this data in a variety of ways to improve sales, save costs, increase customer relationships, reduce risks, and more.

Data mining is important for detecting relationships and analyzing features between data points. Various strategies are utilized to cope with challenges in data mining. In data mining, regression is one of the most important approaches.

This article will show you about Regression Method in Data Mining. It will also provide a deep understanding of Data Mining, Applications, Classification, and Regression Method in Data Mining.

Table of Contents

• What is Data Mining?
  • Key Features of Data Mining
• Why do you need Data Mining?
• What is Regression Method in Data Mining?
• Regression vs Classification in Data Mining
• What are the different applications of the Regression Method in Data Mining?
• Understanding the Regression Method in Data Mining
  • Polynomial
  • Linear
  • Ridge
  • Logistic
  • Lasso
  • ElasticNet
  • Hierarchical
  • Decision Tree
  • Random Forest
• Understanding Oracle Data Mining Algorithms
  • Support Vector Machines (SVM)
  • Generalized Linear Models (GLM)
• What are the Metrics to Evaluate a Regression Model?
• Conclusion

What is Data Mining?

Data Mining has improved corporate decision-making through sophisticated Data Analytics. These investigations’ Data Mining approaches can be divided into two categories: 

• they can either describe the target dataset or 
• predict results using machine learning algorithms. 

These tactics are used to organize and filter data, providing the most important information, from fraud detection to user behaviors, bottlenecks, and even security breaches. When paired with Data Analytics and Visualization technologies like Apache Spark, diving into the domain of Data Mining has never been easier, and collecting significant insights has never been faster. Artificial intelligence (AI) is gaining traction in a variety of areas.

In order to extract meaningful information from enormous data sets, Data Mining comprises a succession of processes, ranging from Data Collection to Visualization. As previously mentioned, data mining techniques are used to generate descriptions and predictions about a specific data set. Data Scientists employ patterns, linkages, and correlations to describe data. They also use classification and regression techniques to classify and cluster data, as well as to identify outliers for applications such as spam detection. The four key aspects of Data Mining include:

• setting objectives, 
• data gathering and preparation, 
• executing Data Mining algorithms, and 
• assessing results.

Key Features of Data Mining

• Sift through the noisy and repetitive noise in your data.
• Allows you to grasp what matters and then use that knowledge to forecast future results.
• Accelerate the rate at which you make well-informed decisions.
• Because their values are expressions of overvalues already contained in the original database tables, the simplest characteristics are those that depend only on a single focus component, such as store or day.
• Aggregation is frequently the result of many attributes. Individual purchases are too fine-grained for prediction since the details of several transactions must be aggregated to a significant attention level. All degrees of attention are frequently aggregated.

Why do you need Data Mining?

The statistics say that: 

• every two years, the amount of data produced doubles and,
• 90% of the digital cosmos is made up of unstructured data. 

However, having more information does not always imply having better expertise. You can use data mining to:

• Sift through your data’s noisy and repetitious noise.
• Understand what’s important, and then use that knowledge to predict potential outcomes.
• Increase the speed at which you can make well-informed decisions.

What is Regression Method in Data Mining?

Regression Method in Data Mining refers to a technique for predicting numerical values in a dataset. The cost of a product or service, as well as other variables, can be forecasted using Regression. It’s also used in a range of industries for business and marketing behavior, environmental modeling, trend research, and financial forecasting. One of the Data Mining approaches is regression.

Regression vs Classification in Data Mining

The concepts of Regression and Classification are very similar. In Data Mining, Classification and Regression are two important prediction problems. If you give a training set of inputs and outputs and learn a function that connects the two, you should be able to predict outputs from new data given inputs. The only distinction is that the outputs in Classification are discrete, but the outputs in Regression are not. However, certain terms are ambiguous, such as “Logistic Regression,” which can refer to either a Classification or a Regression method. As a result, it’s difficult for the user to know when the Classification and Regression Method in Data Mining should be used.

Regression	Classification
Any continuous-valued attribute can be predicted using regression, which is a form of Supervised Machine Learning technique.	The process of assigning predetermined class labels to instances based on their attributes is known as Classification.
The type of the anticipated data is sorted in Regression.	The nature of the anticipated data is unordered in Classification.
There are two types of Regression: Linear Regression and Non-Linear Regression.	Binary Classifiers and Multi-Class Classifiers are the two types of Classification.
The Root Mean Square Error is used in the Regression process to perform the computations.	Calculations are mostly done in the Classification process by measuring efficiency.
Regression Trees, Linear Regression are examples of Regression.	The Decision Tree is an example of a Classification.

What are the different applications of the Regression in Method Data Mining?

• Modeling of Drug Response
• Business and Marketing Planning
• Forecasting or Financial Forecasting 
• Analyzing Patterns or trends 
• Environmental Simulations
• Pharmaceutical Performance ver Time
• Statistical Data Calibration
• Relationship between Physiochemicals
• Analyzing Satellite Images
• Estimation of Crop Production

Understanding the Regression Method in Data Mining

1) Polynomial

A Polynomial Equation is one in which the power of the independent variable is greater than one in the Regression Method in Data Mining. The notion of Polynomial Regression will be better understood with the help of the example below.

Y = a + b * x2 

The best fit line in this Regression is not a straight line like a linear equation; rather, it depicts a curve fitted to all of the data points.

When you’re inclined to minimize your errors by making the curve more complex, using Linear Regression techniques can lead to overfitting. As a result, try to fit the curve to the situation by generalizing it.

2) Linear

Linear Regression is a type of Regression Method in Data Mining that uses a straight line to build a link between the Target Variable and one or more Independent Variables. The Linear Regression equation is represented by the given equation.

Y = a + b*X + e

Where

• The intercept is represented by a.
• The slope of the regression line is represented by b.
• The letter e stands for error.
• The predictor and target variables are represented by X and Y, respectively.
• Multiple linear equations exist when X is made up of more than one variable.

The least squared method is used in linear regression to find the best fit line, which minimizes the total sum of the squares of the deviations from each data point to the regression line. Since all deviations are squared, the positive and negative deviations are not canceled.

3) Ridge

The term “Ridge Regression” is a type of Regression method in Data Mining for analyzing Multicollinear Regression data. The occurrence of a linear correlation between two independent variables is known as multicollinearity.

When the least square estimates are the least biased with significant variance, Ridge Regression occurs, and the results are quite different from the real value. Ridge Regression, on the other hand, reduces mistakes by adding a degree of bias to the estimated Regression Value.

4) Logistic

The logistic regression technique is used when the dependent variable is binary in characters, such as 0 and 1, true or false, success or failure. The Target Value (Y) is a number that spans from 0 to 1 and is mostly utilized in classification tasks. It does not require any Independent or Dependent variables to have a linear relationship, unlike Linear Regression.

5) Lasso

LASSO is a word that stands for “Least Absolute Shrinkage and Selection Operator.” Lasso Regression is Regression Method in Data Mining that employs shrinkage. All data points are reduced towards a center point, commonly known as the mean, in Lasso Regression. Other Regression methods are better suited for basic and sparse models with fewer parameters than the lasso process. For models with multicollinearity, this method of Regression is ideally suited.

6) ElasticNet

ElasticNet is a type of regularised Linear Regression that contains the L2 and L1 penalty functions, which are well-known Penalties. ElasticNet is a kind of Linear Regression that integrates regularisation penalties into the gradient descent during training. For example, Genetic Data Analysis.

7) Hierarchical

Hierarchical Regression can be used to examine if variables of interest describe a statistically significant variance in your Exogenous Variables (i.e. Dependent Variable) after all other factors have been taken into account. This is a model comparison paradigm rather than a statistical approach. For example, healthcare research.

8) Decision Tree

A Decision Tree generates Regression or Classification models in the form of a tree structure. It compresses a large dataset into smaller and smaller portions over time while also building a Decision Tree. The ultimate result is a tree with leaf nodes that contains a set of nodes. For example, civil engineering planning.

9) Random Forest

Random Forest is a Supervised Learning strategy of Regression Method in Data Mining that employs the Ensemble Learning approach. To produce a more accurate forecast than a single model, the ensemble learning strategy integrates predictions from many machine learning approaches. For example, Stock Market Forecasting and Product Recommendations.

Understanding Oracle Data Mining Algorithms

In Oracle, Regression Method in Data Mining support two approaches of Regression. Both algorithms excel at mining big dimensional (number of attributes) data sets, such as commercial and unstructured data.

1) Support Vector Machines (SVM)

SVMs (Support Vector Machines) are a powerful and cutting-edge linear and nonlinear Regression technique. Oracle Data Mining employs SVM for Regression as well as other mining tasks. SVM Regression supports the Gaussian kernel for nonlinear regression and the linear kernel for linear regression. SVM also encourages active learning. For example, Facial Recognition, Speech Recognition, and Text Classification.

2) Generalized Linear Models (GLM)

GLMs (Generalized Linear Models) are linear models commonly used as a Statistical approach to linear modeling. Oracle Data Mining uses GLM for regression and binary classification. GLM offers a variety of coefficient and model statistics and row diagnostics. GLM additionally allows for confidence boundaries. For example, The agriculture weather modeling logistic regression model of GLM predicts consumer affinity.

What are the Metrics to Evaluate a Regression Model?

1) Mean Absolute Error (MAE)

The absolute difference between Actual and Anticipated Values is calculated using the MAE measure, which is a relatively basic statistic. 

To further understand, consider the following scenario: you have input data and output data, and you want to apply Linear Regression to draw the best-fit line. Now you must locate your model’s MAE, which is essentially a mistake created by the model and is referred to as an error. Get the difference between the Actual and Anticipated values, which is an Absolute Error; however, you must first find the mean absolute of the entire dataset.

As a result, add up all of the mistakes and divide by the total number of observations and this is MAE. Since this is a loss, you want the MAE to be as low as possible.

• The MAE value you receive is in the same unit as the output variable.
• It’s the most resistant to outliers.
• Since the graph of MAE is not differentiable, you must use differentiable optimizers such as gradient descent.

from sklearn.metrics import mean_absolute_error
print("MAE",mean_absolute_error(y_test,y_pred))

• To alleviate the shortcoming of MAE, the MSE Metric was created.

2) Mean Squared Error(MSE)

MSE is a widely used and straightforward statistic that accounts for a small change in Mean Absolute Error. Finding the squared difference between the actual and anticipated value is defined as Mean Squared Error. It denotes the difference in squared values between actual and predicted values. To avoid the cancellation of negatives, squaring of values was done.

• Since MSE’s graph is differentiable, it can readily be used as a loss function.
• A squared unit of output is the result of computing MSE. For example, if the output variable is in meter(m), the output you get after computing MSE is in meters squared.
• If the dataset contains outliers, the outliers are penalized the most, and the estimated MSE is larger. In other words, it is not robust against outliers, which was a benefit in MAE.

from sklearn.metrics import mean_squared_error
print("MSE",mean_squared_error(y_test,y_pred))

3) Root Mean Squared Error (RMSE)

The acronym RMSE indicates that it is a simple square root of mean squared error. 

• Since the output value is in the same unit as the desired output variable, loss interpretation is simple.
• When compared to MAE, it is less resistant to outliers.
• NumPy square root function over MSE is required to perform RMSE.

print(“RMSE”,np.sqrt(mean_squared_error(y_test,y_pred)))

Most of the time, people utilize RMSE as an evaluation metric, and RMSE is very popular when working with deep learning approaches.

4) Root Mean Squared Log Error (RMSLE)

The magnitude of error is slowed by taking the log of the RMSE Metric. When creating a model without calling the inputs, the Metric is really useful. In that instance, the outcome will be quite variable. To regulate the RMSE issue, you take the log of the calculated RMSE Error and call it RMSLE. Over RMSE, you must utilize the NumPy log function to perform RMSLE.

print("RMSE",np.sqrt(mean_squared_error(y_test,y_pred)))

It’s a straightforward statistic that most datasets used in Machine Learning contests employ.

5) R Squared (R2)

The R2 Score is a Metric that measures the performance of your model, not the loss in terms of how many wells it performed. As you’ve seen, MAE and MSE are context-dependent, whereas the R2 Score is context-independent. So, using R squared, you can compare a model to a baseline model that none of the other Metrics can provide. In classification problems, you have something similar called a threshold, which is set at 0.5. R2 Score calculates how much better a Regression line is than a mean line. As a result, R2 Score is also known as the Coefficient of Determination or Goodness of Fit.

Suppose If the R2 Score is 0, the above Regression line divided by the mean line equals one, therefore 1-1 is 0. As a result, the fact that both lines are overlapping indicates that the model’s performance is poor, and it is unable to take advantage of the output column.

The second situation is when the R2 score is 1, which indicates the division term is zero, and it occurs when the regression line makes no errors and is perfect. It is not conceivable in the actual world. As a result, you can deduce that as the Regression line approaches perfection, the R2 score approaches one. Furthermore, the model’s performance improves.

The normal condition is when the R2 value is between 0 and 1, such as 0.8, indicating that your model can explain 80% of the variance in data.

from sklearn.metrics import r2_score
r2 = r2_score(y_test,y_pred)
print(r2)

6) Adjusted R Squared

The downside of the R2 Score is that it starts increasing or remains constant as additional characteristics are added to the data, but it never drops since it implies that as more data is added, the variance of the data increases. The issue is that when you add an unimportant feature to the dataset, R2 occasionally starts to increase, which is incorrect. As a result, Adjusted R Squared was created to deal with the dilemma.

Now, as K grows by adding more features, the denominator shrinks, but n-1 remains constant. The R2 Score will remain constant or increase slightly, resulting in an increase in the total answer. When you remove this from one, the final score will decrease. As a result, when you add an irrelevant feature to the dataset, this is the situation.

When you add a relevant feature, the R2 score goes up, 1-R2 goes down, and the denominator goes down as well, so the total term goes down, and when you subtract from one, the score goes up.

n=40
k=2
adj_r2_score = 1 - ((1-r2)*(n-1)/(n-k-1))
print(adj_r2_score)

As a result, this parameter becomes one of the most crucial indicators to consider when evaluating the model.

Conclusion

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