2 Repos
Awesome GitHub RepositoriesStatistical Modeling Frameworks
Wrappers for machine learning algorithms to perform classification and clustering on linguistic features.
Distinct from Statistical Analysis Libraries: The candidates are either theoretical, finance-specific, or database-focused; this is a general-purpose ML integration for NLP.
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Awesome Statistical Modeling Frameworks GitHub Repositories
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- nltk/nltk
nltk/nltk
14,649Auf GitHub ansehenThis project is a comprehensive Python toolkit designed for natural language processing, research, and education. It functions as a linguistic data processor that provides a standardized framework for managing, cleaning, and analyzing large collections of annotated text corpora and lexical resources. The library distinguishes itself through its integration of both symbolic and statistical methods, allowing users to perform complex tasks ranging from rule-based grammar parsing to machine learning-driven classification. It offers a modular pipeline for text processing, enabling the transformati
Wraps various machine learning algorithms to perform classification and clustering tasks on processed linguistic feature sets.
Pythonmachine-learningnatural-language-processingnlp - statsmodels/statsmodels
statsmodels/statsmodels
11,260Auf GitHub ansehenStatsmodels is a comprehensive Python library designed for statistical modeling, econometric research, and data analysis. It provides a robust framework for estimating and diagnosing a wide range of statistical models, enabling users to perform rigorous hypothesis testing, regression analysis, and complex data exploration within structured environments. The library distinguishes itself through its support for advanced statistical methodologies, including state space representation for dynamic systems and generalized linear frameworks that accommodate non-normal response variables. It offers s
Constructs complex relationships between variables using a descriptive formula syntax.
Pythoncount-modeldata-analysisdata-science
