#catboost_tipsntricks resultados de búsqueda
#catboost_tipsntricks Did you know that CatBoost supports feature selection via recursive elimination from version 0.25? As always, interactive graphs are available both for process and final results visualisation. Read more details in our tutorial: github.com/catboost/tutor… 😺
#catboost_tipsntricks Stanislav Kirillov explains how to choose training mode: CPU or GPU @CatBoostML youtube.com/watch?v=VrtSrR…
#catboost_tipsntricks Did you know that CatBoost models has a builtin metadata storage? You can easily write any information about a model inside it from Python or CLI. Check our documentation catboost.ai/docs/concepts/… and catboost.ai/docs/concepts/… 😺
#catboost_tipsntricks CatBoost sets a learning rate by looking at the number of iterations&objects in the trainset. In today's video, Nikita explains how to use built-in interactive learning curves to tune LR & iterations and improve model performance. youtu.be/O2OJ_JWYV0I
#catboost_tipsntricks 📹Model prediction interpretation in a human-readable form is a key for making a great machine learning system. In this video Nikita shows how to use SHAP values to understand model predictions youtu.be/RNT1o2gu5Ms
youtube.com
YouTube
CatBoost | Interpret CatBoost models: built-in tools for understand...
#catboost_tipsntricks In today's video, Nikita Dmitriev talks about object importance and how you can use it to detect and drop noise objects and boost the quality of your models🚀Stay tuned for the next episode! 😺 youtu.be/ce1VULptNWQ
youtube.com
YouTube
CatBoost | Object importance: detect and drop noise objects to...
#catboost_tipsntricks Feature selection is a crucial part of data engineering & ML. In today's video, Ivan talks about CatBoost's built-in feature selection function. It can help you speed up training and reduce overfitting.🚀 youtu.be/iuRGv31mcuI
youtube.com
YouTube
CatBoost | Feature Selection: speedup training and decrease overfit...
#catboost_tipsntricks If you use GBDT models in production, don't miss that video😺 Ekaterina Ermishkina explains how to apply CatBoost models in different formats and environments: native binary format, CoreML, PMML, ONNX, in Java, Rust, NodeJS and others youtu.be/fpUEoy60x24
youtube.com
YouTube
CatBoost | Apply your models everywhere: using and exporting models...
#catboost_tipsntricks Consoles are not only for Jupyter&Python😸 In today's video Kate explains how to use main CatBoost features from CLI. This simple but powerful interface allows you to use practically anywhere and improve ml pipelines. youtu.be/m3E35snIrAM
#catboost_tipsntricks Stanislav Kirillov explains how to choose training mode: CPU or GPU @CatBoostML youtube.com/watch?v=VrtSrR…
#catboost_tipsntricks CatBoost sets a learning rate by looking at the number of iterations&objects in the trainset. In today's video, Nikita explains how to use built-in interactive learning curves to tune LR & iterations and improve model performance. youtu.be/O2OJ_JWYV0I
#catboost_tipsntricks Consoles are not only for Jupyter&Python😸 In today's video Kate explains how to use main CatBoost features from CLI. This simple but powerful interface allows you to use practically anywhere and improve ml pipelines. youtu.be/m3E35snIrAM
#catboost_tipsntricks 📹Model prediction interpretation in a human-readable form is a key for making a great machine learning system. In this video Nikita shows how to use SHAP values to understand model predictions youtu.be/RNT1o2gu5Ms
youtube.com
YouTube
CatBoost | Interpret CatBoost models: built-in tools for understand...
#catboost_tipsntricks Feature selection is a crucial part of data engineering & ML. In today's video, Ivan talks about CatBoost's built-in feature selection function. It can help you speed up training and reduce overfitting.🚀 youtu.be/iuRGv31mcuI
youtube.com
YouTube
CatBoost | Feature Selection: speedup training and decrease overfit...
#catboost_tipsntricks If you use GBDT models in production, don't miss that video😺 Ekaterina Ermishkina explains how to apply CatBoost models in different formats and environments: native binary format, CoreML, PMML, ONNX, in Java, Rust, NodeJS and others youtu.be/fpUEoy60x24
youtube.com
YouTube
CatBoost | Apply your models everywhere: using and exporting models...
#catboost_tipsntricks In today's video, Nikita Dmitriev talks about object importance and how you can use it to detect and drop noise objects and boost the quality of your models🚀Stay tuned for the next episode! 😺 youtu.be/ce1VULptNWQ
youtube.com
YouTube
CatBoost | Object importance: detect and drop noise objects to...
#catboost_tipsntricks Did you know that CatBoost supports feature selection via recursive elimination from version 0.25? As always, interactive graphs are available both for process and final results visualisation. Read more details in our tutorial: github.com/catboost/tutor… 😺
#catboost_tipsntricks Did you know that CatBoost models has a builtin metadata storage? You can easily write any information about a model inside it from Python or CLI. Check our documentation catboost.ai/docs/concepts/… and catboost.ai/docs/concepts/… 😺
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