redframes
xgboost
redframes | xgboost | |
---|---|---|
10 | 10 | |
295 | 25,576 | |
- | 0.4% | |
1.4 | 9.6 | |
about 1 year ago | 6 days ago | |
Python | C++ | |
BSD 2-clause "Simplified" License | Apache License 2.0 |
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redframes
- What is something you wish there was a Python module for?
- Redframes: General Purpose Data Manipulation Library
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Modern Polars: an extensive side-by-side comparison of Polars and Pandas
I'm not GP, but I find the pandas API incredibly inconsistent and difficult to remember how to do simple transformations. For example, it sometimes overloads operators because it doesn't use built in language features like lambdas. There are reasons for the inconsistency, but using the alternatives like R's tidyverse or Julia's DataFramess.jl is like night and day for me.
I found RedFrames [1] recently which wraps Pandas dataframes with a more consistent interface, it's probably what I'd use if I had to write data transformations that had to be compatible with Pandas.
[1] https://github.com/maxhumber/redframes
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Ask HN: How you maintain your daily log?
[2022-10-23 14:11:15]: Question []: should we use Red Frames (https://github.com/maxhumber/redframes) in addition to Pandas? Criteria for decision? @me #projectLion
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Python 3.11.0 final is now available
If you like writing chain-able pandas, you should check out: https://github.com/maxhumber/redframes
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Add your own custom methods to third-party types with this pattern
I intend to use this pattern in my redframes library to hijack some pd.DataFrame methods.
- GitHub - maxhumber/redframes: [re]ctangular[d]ata[frames]
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Ask HN: What are you doing this weekend?
I'm dog-fooding my new Python data manipulation library, redframes: https://github.com/maxhumber/redframes
To help me prep for my Fantasy Hockey Draft next week!
- redframes, a new data manipulation library for ML and visualization
- Show HN: Redframes, a Python data manipulation library like dplyr
xgboost
- XGBoost 2.0
- XGBoost2.0
- Xgboost: Banding continuous variables vs keeping raw data
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PSA: You don't need fancy stuff to do good work.
Finally, when it comes to building models and making predictions, Python and R have a plethora of options available. Libraries like scikit-learn, statsmodels, and TensorFlowin Python, or caret, randomForest, and xgboostin R, provide powerful machine learning algorithms and statistical models that can be applied to a wide range of problems. What's more, these libraries are open-source and have extensive documentation and community support, making it easy to learn and apply new techniques without needing specialized training or expensive software licenses.
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XGBoost Save and Load Error
You can find the problem outlined here: https://github.com/dmlc/xgboost/issues/5826. u/hcho3 diagnosed the problem and corrected it as of XGB version 1.2.0.
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For XGBoost (in Amazon SageMaker), one of the hyper parameters is num_round, for number of rounds to train. Does this mean cross validation?
Reference: https://github.com/dmlc/xgboost/issues/2031
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CS Internship Questions
By the way, most of the time XGBoost works just as well for projects, would not recommend applying deep learning to every single problem you come across, it's something Stanford CS really likes to showcase when it's well known (1) that sometimes "smaller"/less complex models can perform just as well or have their own interpretive advantages and (2) it is well known within ML and DS communities that deep learning does not perform as well with tabular datasets and using deep learning as a default to every problem is just poor practice. However, if you do (god forbid) get language, speech/audio, vision/imaging, or even time series models then deep learning as a baseline is not the worst idea.
- OOM with ML Models (SKlearn, XGBoost, etc), workaround/tips for large datasets?
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xgboost VS CXXGraph - a user suggested alternative
2 projects | 28 Feb 2022
- 'y contains previously unseen labels' (label encoder)
What are some alternatives?
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Keras - Deep Learning for humans
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
tensorflow - An Open Source Machine Learning Framework for Everyone
MLflow - Open source platform for the machine learning lifecycle
pydeep - Deep learning in Python
mlpack - mlpack: a fast, header-only C++ machine learning library
skflow - Simplified interface for TensorFlow (mimicking Scikit Learn) for Deep Learning
catboost - A fast, scalable, high performance Gradient Boosting on Decision Trees library, used for ranking, classification, regression and other machine learning tasks for Python, R, Java, C++. Supports computation on CPU and GPU.