Regression algorithms
Base class for regression modules
Polynomial Regression
Power regression
Simple Linear Regression
Exponential Regression
Multivariate linear regression
Theil-Sen regression
Robust polynomial regression using LMedS
Polynomial Regression 2D
Lasso (least absolute shrinkage and selection operator) Regression
Logistic regression
Fit a model to noisy data by excluding outliers. This is an implementation of the RANSAC algorithm.
CART decision tree algorithm
Iterative regression based baseline correction
Regression transform for Vega dataflows.
This is a copy of ml-regression-multivariate-linear with a minimal change for angular use.
Calculate statistical regressions for two-dimensional data
Matrix manipulation and computation library
Get the maximum value in an array
Various method to process spectra
Random forest for classification and regression
Get the minimum value in an array
Javascript least squares data fitting methods
This Ruby gem leverages Machine Learning(ML) techniques to make predictions(forecasts) and classifications in various applications. It provides capabilities such as predicting next month's billing, forecasting upcoming sales orders, identifying patient's potential findings(like Diabetes), determining user approval status, classifying text, generating similarity scores, and making recommendations. It uses Python3 under the hood, powered by popular machine learning techniques including NLP(Natural Language Processing), Decision Tree, K-Nearest Neighbors and Logistic Regression, Random Forest and Linear Regression algorithms.
Ruby Scientist and Graphics is a practical data science toolkit for Ruby. It includes a lightweight built-in DataFrame for loading, cleaning, and transforming data; quick descriptive statistics and correlations; charting via Gruff (bar and line); and simple ML utilities (linear regression and k-means)—all behind a small, unified, pandas-inspired API. Key features: - Load data from CSV and JSON. - Clean and transform (remove/add columns, handle missing values, limit rows). - Describe datasets and compute correlations quickly. - Create bar and line charts with customization options. - Train/predict with linear regression; cluster with k-means. - Save/load project state (data + trained model) and run simple pipelines. - Optional backend adapters (e.g., Rover) while keeping the same API. Ideal for analysts and developers who want to explore data in Ruby without relying on Python or R. Note: plotting via Gruff uses rmagick, which requires ImageMagick installed on the system.
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