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04-26-2014 03:20 PM

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.

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04-26-2014 06:49 PM

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

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04-26-2014 08:01 PM

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.

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04-26-2014 09:29 PM

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?

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04-27-2014 05:21 PM

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.

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04-27-2014 05:33 PM

No.

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

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04-27-2014 05:35 PM

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

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04-27-2014 05:39 PM

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

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04-27-2014 05:41 PM