catboost
flamegraph
catboost | flamegraph | |
---|---|---|
8 | 47 | |
7,776 | 4,325 | |
1.1% | 2.9% | |
9.9 | 7.4 | |
5 days ago | 6 days ago | |
Python | Rust | |
Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
catboost
- CatBoost: Open-source gradient boosting library
- Boosting Algorithms
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What's New with AWS: Amazon SageMaker built-in algorithms now provides four new Tabular Data Modeling Algorithms
CatBoost is another popular and high-performance open-source implementation of the Gradient Boosting Decision Tree (GBDT). To learn how to use this algorithm, please see example notebooks for Classification and Regression.
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Writing the fastest GBDT libary in Rust
Here are our benchmarks on training time comparing Tangram's Gradient Boosted Decision Tree Library to LightGBM, XGBoost, CatBoost, and sklearn.
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Data Science toolset summary from 2021
Catboost - CatBoost is an open-source software library developed by Yandex. It provides a gradient boosting framework which attempts to solve for Categorical features using a permutation driven alternative compared to the classical algorithm. Link - https://catboost.ai/
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CatBoost Quickstart — ML Classification
CatBoost is an open source algorithm based on gradient boosted decision trees. It supports numerical, categorical and text features. Check out the docs.
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[D] What are your favorite Random Forest implementations that support categoricals
If you considering GBDT check out catboost, unfortunately RF mode is not available but library implement lots of interesting categorical encoding tricks that boost accuracy.
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CatBoost and Water Pumps
The data contains a large number of categorical features. The most suitable for obtaining a base-line model, in my opinion, is CatBoost. It is a high-performance, open-source library for gradient boosting on decision trees.
flamegraph
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Rust Tooling: 8 tools that will increase your productivity
You can install cargo-flamegraph with cargo install flamegraph. There are some underlying requirements to be able to use cargo-flamegraph; you will want to take a look at the repo here to make sure you have the right dependencies.
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Need help making sense of these benchmark results
I tried to diagnose the issue with flamegraph, but unfortunately the flamegraph didn't show anything beyond the next call for some reason
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Why is my code so slow ? advent of code 2022, day 16 (basic graph stuff)
having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go)
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why is my code so slow ? advent of code 2023, day 16 (basic graph stuff)
I'm currently implementing a solution for the first part of the day 16. It work but it is really slow... I'd like to : - understand why - having some tools to identify slowness origins (flamegraph is one... but not sure it's the way to go) - eventually have some clue/solution/idea - have general feedback on what in my "coding style" is not appropriate for rust (I come from java/kotlin/ts even if I've already coded a bit in c/c++) : for example I love iterator & sequence but i feel they are not really suited to overuse in rust (mostly because of async & result).
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how expensive is an operation?
Use a profiler. Flamegraph is a good way to visualise profiler output. This lets you identify which functions are taking up a large amount of time - and hence helps you identify where to focus your optimisation efforts.
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Slow Rust Redis
You tried trying to see what takes the most time under load via flames? https://github.com/flamegraph-rs/flamegraph
- making a virtual machine in rust
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Need help with rust performance
Well, in cases like that the answer is straight forward, use a profiler like https://github.com/flamegraph-rs/flamegraph
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superdiff - a way to find similar code blocks in projects (comments appreciated)
I don't see any obvious problems with your algorithm. I've had luck using cargo-flamegraph to identify the slow parts of my code. That's going to show you which parts to focus on improving the performance of!
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Data-driven performance optimization with Rust and Miri
From the readme of cargo flamegraph:
What are some alternatives?
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
cargo-flamegraph - Easy flamegraphs for Rust projects and everything else, without Perl or pipes <3
Recommender - A C library for product recommendations/suggestions using collaborative filtering (CF)
tracing - Application level tracing for Rust.
Keras - Deep Learning for humans
tensorflow_macos - TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute framework.
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
hashbrown - Rust port of Google's SwissTable hash map
vowpal_wabbit - Vowpal Wabbit is a machine learning system which pushes the frontier of machine learning with techniques such as online, hashing, allreduce, reductions, learning2search, active, and interactive learning.
heaptrack - A heap memory profiler for Linux
mxnet - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Scala, Go, Javascript and more
snmalloc-rs - rust bindings of snmalloc