“…Another application of CatBoost in the field of Traffic Engineering is “Accurate classification for automatic vehicle-type recognition based on ensemble classifiers” by Shvai et al [ 90 ]. The authors cover an interesting ensemble technique that involves Convolutional Neural Networks ( ) [ 91 ], Optical Sensors ( ), and CatBoost.…”
Section: Catboost Applications By Fieldmentioning
confidence: 99%
“… Fig. 7 Image from [ 90 ] depicting ensemble architecture of system for automatic vehicle detection; CNN and OS output are fed to CatBoost …”
Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.
“…Another application of CatBoost in the field of Traffic Engineering is “Accurate classification for automatic vehicle-type recognition based on ensemble classifiers” by Shvai et al [ 90 ]. The authors cover an interesting ensemble technique that involves Convolutional Neural Networks ( ) [ 91 ], Optical Sensors ( ), and CatBoost.…”
Section: Catboost Applications By Fieldmentioning
confidence: 99%
“… Fig. 7 Image from [ 90 ] depicting ensemble architecture of system for automatic vehicle detection; CNN and OS output are fed to CatBoost …”
Gradient Boosted Decision Trees (GBDT’s) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT’s in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost’s effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.
“…However, the first study [85], by Liu et al, is interesting in terms of the new semi-supervised technique presented in it. The second study [75] we cover by Shvai et al shows positive results for CatBoost, and we feel one reason for that is the heterogeneous nature of the data they use in the study. Finally, Spadon et al [76] apply CatBoost to a graph-related problem, and we find only two such graph-related studies, the other being [88], by Yi et al While Spadon et al reject CatBoost for efficiency reasons, it could be worthwhile to investigate whether hyper-parameter optimization would result in improved efficiency for CatBoost.…”
Section: Traffic Engineeringmentioning
confidence: 96%
“…Another application of CatBoost in the field of Traffic Engineering is "Accurate classification for automatic vehicle-type recognition based on ensemble classifiers" by Shvai et al [75]. The authors cover an interesting ensemble technique that involves Convolutional Neural Networks (CNN) [54], Optical Sensors (OS), and Cat-Boost.…”
Section: Traffic Engineeringmentioning
confidence: 99%
“…Image from[75] depicting ensemble architecture of system for automatic vehicle detection; CNN and OS output are fed to CatBoostTable 19From[65], showing the mean tree construction time in seconds; bold text from the original work, indicates shortest tree construction time iteration of a GBDT algorithm is the construction of a Decision Tree, so it would behoove us to know the root cause of the discrepancy in running times that Spadonet al and Prokhorenkova et al report. To be clear about Prokhorenkova et al's claims about CatBoost's and XGBoost's running time performance, we include a copy of a table from Appendix C.2 of "CatBoost: unbiased boosting with categorical features," in…”
Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and regression tasks in Big Data, Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT's in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have ellCcessfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that .55 CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost's effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.
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