All Algorithms implemented in Python
Skill ディレクトリ
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検索結果: automl-algorithms
英語版ディレクトリDive into this repository, a comprehensive resource covering Data Structures, Algorithms, 450 DSA by Love Babbar, Striver DSA sheet, Apna College DSA Sheet, and FAANG Questions! 🚀 That's not all! We've got Technical Subjects like Operating Systems, DBMS, SQL, Computer Networks, and Object-Oriented Programming, all waiting for you.
Turns Data and AI algorithms into production-ready web applications in no time.
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
PyTorch version of Stable Baselines, reliable implementations of reinforcement learning algorithms.
Open-source, low-code AutoML platform for Python. PyCaret 4.0: sklearn-native engine + React control plane.
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.
Uplift modeling and causal inference with machine learning algorithms
AutoRAG: An Open-Source Framework for Retrieval-Augmented Generation (RAG) Evaluation & Optimization with AutoML-Style Automation
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.
A fast library for AutoML and tuning. Join our Discord: https://discord.gg/Cppx2vSPVP.
Scalable and user friendly neural :brain: forecasting algorithms.