Mamba-Tabluar
+Mambular: Tabular Deep Learning with Mamba Architectures
Mambular is a Python package that brings the power of Mamba architectures to tabular data, offering a suite of deep learning models for regression, classification, and distributional regression tasks. Designed with ease of use in mind, Mambular models adhere to scikit-learn's BaseEstimator interface, making them highly compatible with the familiar scikit-learn ecosystem. This means you can fit, predict, and transform using Mambular models just as you would with any traditional scikit-learn model, but with the added performance and flexibility of deep learning.
Features
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Features
+Documentation
+You can find the Mamba-Tabular API documentation here.
+
Installation
Install Mambular using pip:
@@ -177,7 +182,7 @@Available Distribution Classes:MambularLSS offers a wide range of distribution classes to cater to various statistical modeling needs. The available distribution classes include:
normal: Normal Distribution for modeling continuous data with a symmetric distribution around the mean.
poisson: Poisson Distribution for modeling count data that represents the number of events occurring within a fixed interval.
poisson: Poisson Distribution for modeling count data that for instance represent the number of events occurring within a fixed interval.
gamma: Gamma Distribution for modeling continuous data that is skewed and bounded at zero, often used for waiting times.
beta: Beta Distribution for modeling data that is bounded between 0 and 1, useful for proportions and percentages.
dirichlet: Dirichlet Distribution for modeling multivariate data where individual components are correlated, and the sum is constrained to 1.
Getting Started with MambularLSS:
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normal: Normal Distribution for modeling continuous data with a symmetric distribution around the mean.
poisson: Poisson Distribution for modeling count data that represents the number of events occurring within a fixed interval.
poisson: Poisson Distribution for modeling count data that for instance represent the number of events occurring within a fixed interval.
gamma: Gamma Distribution for modeling continuous data that is skewed and bounded at zero, often used for waiting times.
beta: Beta Distribution for modeling data that is bounded between 0 and 1, useful for proportions and percentages.
dirichlet: Dirichlet Distribution for modeling multivariate data where individual components are correlated, and the sum is constrained to 1.