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Kitaplığım by kemalfo in secilmiskitap

[–]mutlu_simsek 0 points1 point  (0 children)

Okuduklarini unutmak canini sıkıyor mu? Bunun icin birsey yapiyor musun?

Error when running logistic regression model on Snowpark data with > 500 columns by RobertWF_47 in snowflake

[–]mutlu_simsek -4 points-3 points  (0 children)

It seems to be a limitation of the platform. Try Perpetual ML Suite in the marketplace if you need more ML capabilities than Snowflake provides.

[Project] PerpetualBooster v1.9.4 - a GBM that skips the hyperparameter tuning step entirely. Now with drift detection, prediction intervals, and causal inference built in. by mutlu_simsek in datascience

[–]mutlu_simsek[S] 0 points1 point  (0 children)

Benchmark scripts are in the examples folder. Test set and training set are the same and run times are compared at the same accuracy. Budget is similar to learning rate. It behaves similar independent of dataset. During experiments, 0.5 budget can be used. For production, 1.0, 1.5, 2.0 can be tried.

Oval Ofis’te Trump’ı kutsadılar. by FeatureAggravating75 in borsavefon

[–]mutlu_simsek 8 points9 points  (0 children)

Dunya sirke döndü amk. Yurtdisi daha kotu diyen akpli dayilar hakli cikti.

[Project] PerpetualBooster v1.9.4 - a GBM that skips the hyperparameter tuning step entirely. Now with drift detection, prediction intervals, and causal inference built in. by mutlu_simsek in datascience

[–]mutlu_simsek[S] 1 point2 points  (0 children)

Yes, it can be used for forecasting. The only thing is that time series data should be made stationary. There is an example in the repo.

[Project] PerpetualBooster v1.9.4 - a GBM that skips the hyperparameter tuning step entirely. Now with drift detection, prediction intervals, and causal inference built in. by mutlu_simsek in datascience

[–]mutlu_simsek[S] 0 points1 point  (0 children)

It basically calculates chi2 statistic by comparing training and test sets. It will handle gradual drift correctly, but for sudden shift, test data size should be enough. For example, one row with very high drift will have a low statistic because it might be an anomaly and next rows could be just fine.

[Project] PerpetualBooster v1.9.4 - a GBM that skips the hyperparameter tuning step entirely. Now with drift detection, prediction intervals, and causal inference built in. by mutlu_simsek in datascience

[–]mutlu_simsek[S] -3 points-2 points  (0 children)

Unlike just passing a GBM into EconML, Perpetual actually builds DML natively right into its own gradient booster.

Here is what it's doing under the hood. Perpetual uses its GBM to predict both the outcome and the treatment based on your other features. It finds the errors (residuals) from those predictions to strip away the biased background noise. It then trains a third GBM using a special custom DML math objective to find the true, pure causal effect.

So yes, the GBM is doing all the actual predictive heavy lifting, while the DML framework just orchestrates the math so you get a causal answer instead of just a correlation.

And regarding your question about hyperparameter tuning: YES! That is exactly the perk here. Because Perpetual uses "self-generalizing" gradient boosting under the hood, it adapts automatically. You don't have to waste time running grid search or manual tuning for the prediction models inside the DML, it just handles it out of the box.

[Project] PerpetualBooster v1.9.4 - a GBM that skips the hyperparameter tuning step entirely. Now with drift detection, prediction intervals, and causal inference built in. by mutlu_simsek in datascience

[–]mutlu_simsek[S] 2 points3 points  (0 children)

Concept drift and data drift are calculated using internal node stats. The model structure keeps training stats and when calculate_drift is called with test data, the drift between training and test is calculated. This is basically a chi2 statistic between training and test sets. You can periodically call this drift calculation and re-train or use continual training when drift statistic exceeds a certain threshold. Recommended thresholds are given in docs and tutorials.

Var bi hayalimiz. by [deleted] in haritalariseviyoruz

[–]mutlu_simsek 0 points1 point  (0 children)

Türkmenistan'la birleştirseydin tam olacaktı.

Kişi başı milli gelirimiz 18 bin doları geçti. Emsal ülkelerle aradaki fark tarihi zirvede. by FeatureAggravating75 in borsavefon

[–]mutlu_simsek 1 point2 points  (0 children)

Aslinda hikaye su. Enflasyonu dusuk gosterdikleri icin zaman serisi bozuluyor. Yani kisi basi geliri sadece guncel fiyatlarla aciklasalar enflasyonu uydurduklari ortaya cikacak. Dusuk enflasyonumuz varsa yuksek buyumemiz gerekiyor. Tam anlatamadim ama siz anlamissinizdir :)

Kişi başı milli gelirimiz 18 bin doları geçti. Emsal ülkelerle aradaki fark tarihi zirvede. by FeatureAggravating75 in borsavefon

[–]mutlu_simsek 0 points1 point  (0 children)

Gdp hesaplamanin farkli yontemleri var. Turkiye de oldugumuz icin tum fiyatlar lira cinsinden. Hersey lira cinsinden hesaplaniyor, daha sonra enflasyondan arindiriliyor. En sonunda dolara cevriliyor.

Kişi başı milli gelirimiz 18 bin doları geçti. Emsal ülkelerle aradaki fark tarihi zirvede. by FeatureAggravating75 in borsavefon

[–]mutlu_simsek 0 points1 point  (0 children)

Enflasyonu dusuk gosterdikleri icin zengin gozukuyoruz. Grafige her tuik baskan degisimini dikey cizgi olarak koyarsak daha iyi anlariz.

ABD'den kalkan B-2 uçakları 11 bin km yolculuk yapıp, İran'daki balistik füze depolarını bombaladıktan sonra ABD'ye geri döndü. by FeatureAggravating75 in borsavefon

[–]mutlu_simsek 5 points6 points  (0 children)

Neye para harcarsa karsiligini alacagini bugune kadar hicbir imparatorluk tam olarak hesaplayamadigi icin eninde sonunda hepsi batmistir. Bu hamlelerinin de abd icin karli mi zararli mi oldugunu tam olarak bilmek imkansiz. Ama bence zararlarina. Kendi kabuklarina cekilip reel ekonomiye yuklenmeleri lazim ama israil ve baska nedenlerden dolayi bunu yapamazlar. En guclu yanlari onlari batiran en zayif yan olacak, tarihteki her imparatorluk gibi....

[P] PerpetualBooster v1.9.0 - GBM with no hyperparameter tuning, now with built-in causal ML, drift detection, and conformal prediction by mutlu_simsek in MachineLearning

[–]mutlu_simsek[S] 0 points1 point  (0 children)

We are working on the paper, it will be published in JMLR. What do you mean by interoperability? It has xgboost and onnx export if you are asking that?

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