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 ...
- 77.2k
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/...
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 ...
- 68
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 ...
- 77.2k
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 ...
- 63
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 ...
- 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 ...
- 99.6k
Only top scored, non community-wiki answers of a minimum length are eligible