Xgboost Libraries

15 Components & Libraries

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Xgboost Libraries

Python code for common Machine Learning Algorithms

Python codes for common Machine Learning Algorithms

A library for debugging/inspecting machine learning classifiers and explaining their predictions

ELI5 is a Python package which helps to debug machine learning classifiers and explain their predictions. It provides support for the following machine learning frameworks and packages: License is MI…

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

The algorithms studied are in various commonly used open source implementations like In summary, we are focusing on which algos/implementations can be used to train relatively accurate binary classif…

[UNMAINTAINED] Automated machine learning for analytics & production

Automated machine learning for production and analytics auto_ml is designed for production. Here's an example that includes serializing and loading the trained model, then getting predictions on sing…

MLBox is a powerful Automated Machine Learning python library.

MLBox has been developed and used by many active community members. Your help is very valuable to make it better for everyone.

Easy hyperparameter optimization and automatic result saving across machine learning algorithms and libraries

Automatically save and learn from Experiment results, leading to long-term, persistent optimization that remembers all your tests. HyperparameterHunter provides a wrapper for machine learning algorit…

Scalable machine 🤖 learning for time series forecasting.

Store your time series in a pandas dataframe in long format, that is, each row represents an observation for a specific serie and timestamp. 280 rows × 5 columns

H2O.ai Machine Learning Interpretability Resources

Machine learning algorithms create potentially more accurate models than linear models, but any increase in accuracy over more traditional, better-understood, and more easily explainable techniques i…

Machine learning models for time series analysis

A collection of different Machine Learning models predicting the time series, concretely the market price for given the currency chart and target. Tested with python versions: 2.7.14, 3.6.0. By defau…

State-of-the art Automated Machine Learning python library for Tabular Data

Classifier: Regression: DataPrepare: Simple Models Wrapper: More examples in the folder ./examples: It integrates many popular frameworks: Run

Tutoriais de Python, Data Science, Machine Learning e Deep Learning - Sigmoidal

Veja os tutoriais publicados do Sigmoidal: O Sigmoidal foi uma iniciativa pessoal para divulgar e tornar mais acessível conteúdos da área de Inteligência Artificial e Python. Tenho me comprometido 10…

A repository for some of the code I used in kaggle data science & machine learning tasks.

This repository contains some of the code I use in Kaggle data science/machine learning tasks. Where appliciable I keep all the code/data from a single competition within one a folder. i.e. 'californ…

A Machine Learning API with native redis caching and export + import using S3. Analyze entire datasets using an API for building, training, testing, analyzing, extracting, importing, and archiving. This repository can run from a docker container or from the repository.

Analyzing the IRIS dataset with Sci-Pype This notebook shows how to make new predictions with cached IRIS Classifier Models + Analysis housed in redis. This notebook shows how to make new predictions…

Earthquake Prediction Challenge with LightGBM and XGBoost

Classifying Audio to Emotion

This notebook includes code for reading in audio data, feature extraction, hyperparameter tuning with Optuna, and models including KNN, Logistic Regression, Decision Tree, Boosting, Bagging, Multilay…

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