A Data Science Central Community

i am carrying out logistic regression over 1 set of data. for the test data should i use the model which is developed or use the test data also to build the model.

Added by Minethedata on November 28, 2010 at 12:09am — 2 Comments

Suppose while modeling the training data we use clustering initially to group together objects and then apply decision trees for the data which belongs to cluster 1. Then for the test data how do we conclue that this data belongs to which cluster or do we have to carry out clustering again along with the training data and how do we apply the decision trees which we used for cluster 1?

Added by Minethedata on November 25, 2010 at 5:21am — 3 Comments

Trying to compare 2 methods for churn prediction . 1st method is based on churn attrition calculation by extrapolating churn attrition rate and 2nd method is based on churn prediction by logistic regression. Obvious difference is that the fiurst method gives the headcount but the second method can give individual prediction. Another is that 2nd method is more realistic as it is based on number of realistic variables. Interested to know some more comparisons.

Added by Minethedata on November 13, 2010 at 1:20am — 4 Comments

can we find out which variables are important for carrying out logisitc regression before carrying out logistic regression?

Added by Minethedata on October 18, 2010 at 9:17am — 10 Comments

© 2021 TechTarget, Inc. Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions