xgboost
scikit-learn
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| xgboost | scikit-learn | |
|---|---|---|
| 6 | 64 | |
| 23,872 | 53,380 | |
| 0.7% | 1.3% | |
| 9.3 | 9.9 | |
| 7 days ago | 5 days ago | |
| C++ | Python | |
| Apache License 2.0 | BSD 3-clause "New" or "Revised" License |
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.
xgboost
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xgboost VS CXXGraph - a user suggested alternative
2 projects | 28 Feb 2022
scikit-learn
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We are the developers behind pandas, currently preparing for the 2.0 release :) AMA
There's an issue here about that https://github.com/scikit-learn/scikit-learn/discussions/25450
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Machine learning with Julia - Solve Titanic competition on Kaggle and deploy trained AI model as a web service
This is not a book, but only an article. That is why it can't cover everything and assumes that you already have some base knowledge to get the most from reading it. It is essential that you are familiar with Python machine learning and understand how to train machine learning models using Numpy, Pandas, SciKit-Learn and Matplotlib Python libraries. Also, I assume that you are familiar with machine learning theory: types of machine learning problems like regression and classification, the concept and process of Supervised machine learning (fit/predict and evaluate quality using metrics) and common models used for it, including Random Forest Classifier, and it's implementation in SciKit-Learn Python library. Additionally, it would be great if you previously participated in Kaggle competitions, because to understand and run all code of this article you need to have an account on https://kaggle.com.
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Best Websites For Coders
Scikit-learn : A Python module for machine learning build on top of SciPy
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scikit-learn VS Rath - a user suggested alternative
2 projects | 12 Jan 2023
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Boston Dataset was Removed from scikit-learn 1.2
Can you really call this "banning the dataset"? https://github.com/scikit-learn/scikit-learn/commit/8a86e219...
- ML Frameworks
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Machine Learning Pipelines with Spark: Introductory Guide (Part 1)
The concepts are similar to the Scikit-learn project. They follow Spark’s “ease of use” characteristic giving you one more reason for adoption. You will learn more about these main concepts in this guide.
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How do you programmers make sense of production-level code?
If you look at the README for scikit-learn on GitHub, they say this.
Take a smaller segment to look at. Opening up the front page to a Github repo can be quite daunting. https://github.com/scikit-learn/scikit-learn
What are some alternatives?
Keras - Deep Learning for humans
Prophet - Tool for producing high quality forecasts for time series data that has multiple seasonality with linear or non-linear growth.
Surprise - A Python scikit for building and analyzing recommender systems
MLP Classifier - A handwritten multilayer perceptron classifer using numpy.
tensorflow - An Open Source Machine Learning Framework for Everyone
gensim - Topic Modelling for Humans
H2O - H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
PyBrain
seqeval - A Python framework for sequence labeling evaluation(named-entity recognition, pos tagging, etc...)