2 votes
Accepted

Should I choose an ARIMA model (2,1,1) with a higher AIC value or an ARIMA model (6,1,8) with a lower AIC value?

If your goal is just to forecast (which, if you are using an ARIMA model then my guess is that's the goal), then out of these two competing models you have quite large evidence to suggest that the ...
aranglol's user avatar
  • 2,196
2 votes
Accepted

Would time series input work in multiple polynomial regression model?

Can you practically use Polynomial Regression on time series data? YES YOU CAN! But that does not mean you should! Time series data and non time series data are 2 very different kind. The models which ...
spectre's user avatar
  • 2,065
1 vote

Why some ML models can't take advantage of text ordering information?

The accepted answer is good, but I wanted more. First I should say this is a good guess, but it's not correct AFAIK: Is it because they can't accept floats on their input? Both types of data ...
Nate Anderson's user avatar
1 vote
Accepted

Selecting optimal regression model using cross validation

I am going to go a different route in terms of advice. If your goal is to do statistical inference (i.e. interpret coefficients, see if the data sugggests a causal relationship, etc.) then there is no ...
aranglol's user avatar
  • 2,196
1 vote
Accepted

Aggregating decision criteria of different scales

I'm not sure what you have against majority voting. Clearly you have an ensemble of weak classifiers. Of the many ways to combine them, voting is a nice one, easily explained to stake holders. You ...
J_H's user avatar
  • 1,035
1 vote

How to build a categorization system without a target variable?

Welcome to Data Science! The first step is to make clear for yourself and future models the output you are looking for. It appears it's clear in your mind which tests to priorities but it's not in the ...
fswings's user avatar
  • 378
1 vote
Accepted

How to compare test vs train model performance

When comparing models, the main objective is often to choose the one that performs well on unseen data, that is, the model that has a good generalization ability. This means you'd typically prefer the ...
Dipanwita Mallick's user avatar

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