1 / 20

Aravali College of Engineering and Management, Faridabad

Session on regression in machine learning

aasthakohli04
Télécharger la présentation

Aravali College of Engineering and Management, Faridabad

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Program Name : B.Tech CSESemester : 5thCourse Name: Machine LearningCourse Code:PEC-CS-D-501 (I)Facilitator Name: Aastha

  2. Introduction to RegressionAnalysis • Regression analysis is used to: • Predict the value of a dependent variable based on the value of at least one independentvariable • Explain the impact of changes in an independent variable on the dependentvariable • Dependentvariable: the variable we wish to predict orexplain • Independentvariable: the variable used toexplain • the dependentvariable

  3. Simple LinearRegression Model • Only one independent variable,X • Relationshipbetween X and Y is described by a linearfunction • Changesin Y are assumed to be caused bychangesin X

  4. Types ofRelationships Linearrelationships Curvilinearrelationships Y Y X X Y Y X X

  5. Types ofRelationships (continued) Strongrelationships Weakrelationships Y Y X X Y Y X X

  6. Types ofRelationships (continued) Norelationship Y X Y X

  7. Simple LinearRegression Model Random Error term Population Slope Coefficient Population Y intercept Independent Variable Dependent Variable Yi β0 β1Xi Linearcomponent • εi • RandomError component

  8. Simple LinearRegression Model (continued) Yi β0 β1Xi εi Y ObservedValue of Y forXi εi Slope =β1 PredictedValue RandomError of Y forXi for this Xvalue i Intercept =β0 X Xi

  9. Simple Linear Regression Equation (PredictionLine) The simple linear regression equation provides an estimate of the population regressionline Estimated (orpredicted) Y valuefor observationi Estimate of theregression Estimate of the regressionslope intercept Value of Xfor observationi Yˆi b0 b1Xi The individual randomerrorterms ei have a mean ofzero

  10. Sample Data for House Price Model

  11. Regression UsingExcel • Tools / Data Analysis / Regression

  12. Assumptions ofRegression • Use the acronymLINE: • Linearity • The underlying relationship between X and Y islinear • Independence ofErrors • Error values are statisticallyindependent • Normality ofError • Error values (ε) are normally distributed for any given value of X • Equal Variance(Homoscedasticity) • The probability distribution of the errors has constantvariance Department of Statistics, ITSSurabaya

  13. Pitfalls of RegressionAnalysis • Lacking an awareness of the assumptions underlying least-squaresregression • Not knowing how to evaluate theassumptions • Not knowing the alternatives to least-squares regression if a particular assumption isviolated • Using a regression model without knowledge of the subject matter • Extrapolating outside the relevantrange Department of Statistics, ITSSurabaya

  14. Aravali College of Engineering And Management Jasana, Tigoan Road, Neharpar, Faridabad, Delhi NCR Toll Free Number : 91- 8527538785 Website : www.acem.edu.in

More Related