- Ruby Polars Repo/Docs
- Polars API reference
- Vega -- Graphing library to use with Polars
- Awesome Machine Learning with Ruby repo
- Andrew Kane's ML Gems
- Landon Gray's introduction to Machine Learning with Ruby
- Justin Bowen's Discover Machine Learning in Ruby
- Machine learning Docker images for Ruby ml-stack
- Rumale (Ruby machine learning) is a machine learning library in Ruby. Rumale provides machine learning algorithms with interfaces similar to Scikit-Learn in Python.
- ONNX Runtime Ruby high performance scoring engine for ML models - for Ruby
- Torch.rb and TensorFlow
- TorchVision for computer vision tasks
- TorchText for text and NLP tasks
- TorchAudio for audio tasks
- TorchRec for recommendations
- Prophet.rb is a forecasting library. It supports multiple seasonalities, holidays, growth caps, and many other features.
- MITIE does named-entity recognition. It finds people, organizations, and locations in text.
- IRuby
- Jupyter
- Google Colab
- Host Jupyter Notebooks online mybinder
- SciRuby
- Outlier Detection and Removal Though in Python it's a good high level introduction to the statistical concepts. Introduces you to IQR and Standard Deviation, the two main outlier removal types.
# Std Deviation - filtering 2 std deviations away
df = Polars::DataFrame.new({'example' => [10, 20, 30, 40, 50, 10000]})
mean = df['example'].mean
std = df['example'].std
outliers = (df['example'] < mean + (2 * std)) & (df['example'] > mean - (2 * std))
removed = df.filter(outliers)
#one liner
removed = df.filter((df['example'] < df['example'].mean + (2 * df['example'].std)) & (df['example'] > df['example'].mean - (2 * df['example'].std)))
#IQR
series = Polars::Series.new('example', [-2.08, 1.9, -5.70, 7.08, 0.73, -3.50, 2.57, 0.21, -9.26])
series.quantile(0.75, interpolation:"nearest")