11 votes

Do Statistical Binning Algorithms Exist?

The most rational and elegant solution, and best performing in terms of mean squared error of estimates, is to use a method that borrows information across groups: either penalized maximum likelihood ...
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2 votes

overfitting of random forest in r

How does the 70 % accuracy of your CV compare to the rF's oob estimate? The behaviour you observe is to be expected for random forests, see also my old answer: https://stats.stackexchange.com/a/...
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1 vote

Partial dependency for random forest when predictive accuracy is bad

A partial dependence plot (PDP) should be valid as a visualization of the marginal effect that your features have on the predicted outcomes of your model, regardless of the predictive accuracy of the ...
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1 vote

OVerfitting using Random Forest - classification

Random forests are notorious for overfitting. Univariate screening of variables before running RF is not at all appropriate. RF (which requires enormous sample sizes to be successful) requires ...
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1 vote

overfitting of random forest in r

Random Forest Classifiers (RFC) with 100% training accuracy are not necessarily problematic. Make sure you are optimizing your hyperparameters on a separate validation set, this is especially ...
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1 vote

Fine-tune random forest on time series

You could give a dev package I wrote up a shot. It just wraps LightGBM (boosted trees) with some time series functionality such as fourier basis functions, 'custom' linear basis functions, and ar ...
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  • 1,365
1 vote
Accepted

Fine-tune random forest on time series

You can always include lagged values of the target variable to account for autocorrelation. However, for a Boolean target, that will likely not add a lot of value. Also, much of the autoregressive ...
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