Data Mining. 3.6 Regression Analysis. Fall Instructor: Dr. Masoud Yaghini. Numeric Prediction
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1 Data Mining 3.6 Regression Analysis Fall 2008 Instructor: Dr. Masoud Yaghini
2 Outline Introduction Straight-Line Linear Regression Multiple Linear Regression Other Regression Models References
3 Introduction
4 Introduction Numerical prediction is similar to classification construct a model use model to predict continuous or ordered value for a given input Prediction is different from classification Classification refers to predict categorical class label Prediction models continuous-valued functions Major method for prediction: regression model the relationship between one or more independent or predictor variables and a dependent or response variable
5 Introduction In the context of data mining the predictor variables are the attributes of interest describing the tuple that are known. The response variable is what we want to predict Many texts use the terms regression and numeric prediction Some classification techniques can be adapted for prediction. e.g. backpropagation, support vector machines, and k-nearest-neighbor classifiers
6 Introduction Regression analysis is a good choice when all of the predictor variables are continuous valued as well. Regression analysis methods Linear regression Straight-line linear regression Multiple linear regression Non-linear regression Generalized linear model Poisson regression Logistic regression Log-linear models Regression trees and Model trees
7 Straight-Line Linear Regression
8 Linear Regression Straight-line linear regression: involves a response variable y and a single predictor variable x y = w 0 + w 1 x where w 0 (y-intercept) and w 1 (slope) are regression coefficients
9 Linear regression Method of least squares: estimates the best-fitting straight line as the one that minimizes the error between the actual data and the estimate of the line. D: a training set x: consisting of values of predictor variable y: response variable D : data points of the form(x1, y1), (x2, y2),, (x D, y D ). : the mean value of x1, x2,, x D : the mean value of y1, y2,, y D
10 Example: Salary problem The table shows a set of paired data where x is the number of years of work experience of a college graduate and y is the corresponding salary of the graduate.
11 Linear Regression The 2-D data can be graphed on a scatter plot. The plot suggests a linear relationship between the two variables, x and y.
12 Example: Salary data Given the above data, we compute we get the equation of the least squares line is estimated by
13 Example: Salary data Linear Regression: Y=3.5*X Salary Years
14 Multiple Linear Regression
15 Multiple linear regression Multiple linear regression involves more than one predictor variable Training data is of the form (X 1, y 1 ), (X 2, y 2 ),, (X D, y D ) where the X i are the n-dimensional training data with associated class labels, y i An example of a multiple linear regression model based on two predictor attributes:
16 Example: CPU performance data
17 Multiple Linear Regression Various statistical measures exist for determining how well the proposed model can predict y. (described later) Obviously, the greater the number of predictor attributes is, the slower the performance is. Before applying regression analysis, it is common to perform attribute subset selection to eliminate attributes that are unlikely to be good predictors for y. In general, regression analysis is accurate for prediction, except when the data contain outliers.
18 Other Regression Models
19 Nonlinear Regression Sometimes we can get a more accurate model using a nonlinear model, For example, y = w 0 + w 1 x + w 2 x 2 + w 3 x 3 Some nonlinear models can be transformed into linear regression model. For example, the above function can be converted to linear with new variables: x 2 = x 2, x 3 = x 3 y = w 0 + w 1 x + w 2 x 2 + w 3 x 3
20 Generalized linear models Generalized linear model is foundation on which linear regression can be applied to modeling categorical response variables Common types of generalized linear models include Logistic regression: Models the probability of some event occurring as a linear function of a set of predictor variables. Poisson regression: models the data that exhibit a Poisson distribution
21 Log-linear models In the log-linear method, all attributes must be categorical Continuous-valued attributes must first be discretized.
22 Regression Trees and Model Trees Trees to predict continuous values rather than class labels Regression and model trees tend to be more accurate than linear regression when the data are not represented well by a simple linear model
23 Regression trees Regression tree: a decision tree where each leaf predicts a numeric quantity Proposed in CART system (Breiman et al. 1984) CART: Classification And Regression Trees Predicted value is average value of training instances that reach the leaf
24 Example: CPU performance problem Regression tree for the CPU data
25 Example: CPU performance problem We calculate the average of the absolute values of the errors between the predicted and the actual CPU performance measures It turns out to be significantly less for the tree than for the regression equation.
26 Model tree Model tree: Each leaf holds a regression model A multivariate linear equation for the predicted attribute Proposed by Quinlan (1992) A more general case than regression tree
27 Example: CPU performance problem Model tree for the CPU data
28 References
29 References J. Han, M. Kamber, Data Mining: Concepts and Techniques, Elsevier Inc. (2006). (Chapter 6) I. H. Witten and E. Frank, Data Mining: Practical Machine Learning Tools and Techniques, 2nd Edition, Elsevier Inc., (Chapter 6)
30 The end
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