5.Regressions-Generic.pdf

# 5.Regressions-Generic.pdf - Licensed for personal use only...

• 66

This preview shows page 1 - 9 out of 66 pages.

Session: Regressions Regression Algorithms Linear Regression Logistic Regression Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20 Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20

Subscribe to view the full document.

Regressions è Regression Algorithms Linear Regression Logistic Regression Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20 Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20
© 2017 ElephantScale.com. All rights reserved. What is Regression Analysis u Regression models relationship between independent variable(s) (predictor) and dependent variable (target) u Regressions are used to predict 'numeric' data – House prices – Stock price 3 Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20 Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20

Subscribe to view the full document.

© 2017 ElephantScale.com. All rights reserved. Regression Algorithms Algorithm Description Use Case Linear Regression Establishes a best fit 'straight line' Advantages: - Simple, well understood - Scales to large datasets Disadvantages - Prone to outliers - House prices - Stock market Logistic Regression - Calculates the probability of outcome (success or failure) - Used for 'classification' J - Needs large sample sizes for accurate prediction - Mortgage application approval 4 Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20 Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20
© 2017 ElephantScale.com. All rights reserved. Regression Algorithms Algorithm Description Use Case Polynomial Regression If power of independent variable is more than 1. Y = a + b * X 2 - Can be prone to overfitting - Results can be hard to explain Stepwise Regression - When we have multiple independent variables, automatically selects significant variables - No human intervention - AIC - House price predictor 5 Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20 Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20

Subscribe to view the full document.

© 2017 ElephantScale.com. All rights reserved. Regression Algorithms Algorithm Description Use Case Ridge Regression - used when independent variables are highly correlated - Uses L2 regularization Lasso Regression - Uses L1 regularization ElasticNet Regression - Hybrid of Lasso and Ridge regressions 6 Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20 Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20
Linear Regression Regression Algorithms è Linear Regression Logistic Regression Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20 Licensed for personal use only for Sriram Anne <[email protected]> from Python @ Macy's 2018-02-20 @ 2018-02-20

Subscribe to view the full document.

{[ snackBarMessage ]}

###### "Before using Course Hero my grade was at 78%. By the end of the semester my grade was at 90%. I could not have done it without all the class material I found."
— Christopher R., University of Rhode Island '15, Course Hero Intern

### What students are saying

• As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

Kiran Temple University Fox School of Business ‘17, Course Hero Intern

• I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

Dana University of Pennsylvania ‘17, Course Hero Intern

• The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

Jill Tulane University ‘16, Course Hero Intern