Image via WikipediaThis list of John Elder, Ph.D is not so recent any more, but is worth looking at every once in a while in order not to forget them :

1. Lack Data
2. Focus on Training
3. Rely on One Technique
4. Ask the Wrong Question
5. Listenen (only) to the Data
6. Accept Leaks from the Future
7. Discount Pesky Cases
8. Extrapolate
9. Answer Every Inquiry
10. Sample Casually
11. Believe the Best Model.

(the counting error is John Elders’, not mine!)

I destilled also a more recenter list from this splendid paper of Doug Wielenga on the 2007 SAS forum :

1. failing to consider enough variables
2. incorrectly preparing or failing to prepare categorical predictors
3. incorrectly preparing or failing to prepare continuous predictors
4. ignoring or misusing time-dependent information
5. inappropriate metadata
6. inadequate or excessive input data
7. inappropriate or missing target profile for categorical target
8. target variable event levels occuring in different proportions
9. differences in misclassification costs
10. misunderstanding the roles of the partitioned data sets
11. failing to consider changing the default partition
12. failing to evaluate the variables before selection
13. using only one selection method
14. misunderstanding or ignoring variable selection options
15. choosing settings in the chi-squared or R-squared mode
16. failing to evaluate imputation method
17. overlooking missing value indicators
18. overusing stepwise regression
19. inaccurately interpreting the results
20. ignoring tree instability
21. ignoring tree limitations
22. failing to do variable selection
23. failing to consider neural networks
24. misinterpreting lift
25. chosing the wrong assessment statistic
26. generating inefficient score code
27. ignoring the model performance
28. building one cluster solution
29. including (many) categorical variables
30. failing to sort the (assessment) data set
31. failing to manage the number of outcomes

Two totally different lists. Has data mining changed that much ? No people have learnt in the meantime and also : the first one is more general : do not misuse data-mining, the second one is more technical : when you do it, do it good !
Both papers are worth reading ! Enjoy!

Related articles by Zemanta

• Change the algorithm, not the dataset

Did you liked this post ? Then you might be interested in the following :
Oversampling or undersampling ?
data mining with decision trees : what they never tell you
Mining highy imbalanced data sets with logistic regressions
Howmany inputs do data miners need ?