Average Square Error is too high.

[NareshAbburi]

Hi,

I'm building a model which is giving me the very high average square error and misclassification rate.

Ho can I reduce these two results. Please provide me valuable inputs.

Also, Please let me know what is basic flow for Data Mining Model.

Thanks.

[Reeza]

That's way too difficult to answer here.

Your model is clearly not fitting well, so try changing the variables included in the model.

To learn more about data mining perhaps look into the CRISP-DM framework and/or check out the data mining courses offered on Coursera, EdX, Udacity for starters.

Lecture Notes | Data Mining | Sloan School of Management | MIT OpenCourseWare

[NareshAbburi]

Hi,

I've tried with multiple combinations, but still ASE is too high. it is nearly 2000. and my validation ASE also nearly 2000.

Is there any alternative to fit my model well.

Thanks.

[Reeza]

without any context its hard to say. How many variables do you have? What is your predictor? How many categorical variables are there? How many continuous? Have you standardized your variables? Or transformed them? What did the univariate analysis show?  Are the scales of your variables incredibly different?

[NareshAbburi]

Hi,

I've a continuous target variable. Input variables are both continuous and categorical variables. (Continuous - 4, Categorical - 3)

I'm trying to build logistic regression. (Will it work...?)

I've standardized and applied transformation also to reduce skewness of the variables.

Plz suggest.

[Reeza]

No.

Logistic regression is for a binary target variable. Linear Regression is for a continuous target variable.

[NareshAbburi]

Then what is best prediction model to apply for combination of categorical and continuous inputs..?

[Reeza]

Linear regression. The model is more dependent on the output required than the input.

[Reeza]

Choosing the Correct Statistical Test in SAS, Stata and SPSS