Machine learning for Ruby

  • Build predictive models quickly and easily
  • Serve models built in Ruby, Python, R, and more
  • No prior knowledge of machine learning required :tada:

Check out this post for more info on machine learning with Rails

Build Status


Add this line to your application’s Gemfile:

gem 'eps'

On Mac, also install OpenMP:

brew install libomp

Getting Started

Create a model

data = [
  {bedrooms: 1, bathrooms: 1, price: 100000},
  {bedrooms: 2, bathrooms: 1, price: 125000},
  {bedrooms: 2, bathrooms: 2, price: 135000},
  {bedrooms: 3, bathrooms: 2, price: 162000}
model = Eps::Model.new(data, target: :price)
puts model.summary

Make a prediction

model.predict(bedrooms: 2, bathrooms: 1)

Store the model

File.write("model.pmml", model.to_pmml)

Load the model

pmml = File.read("model.pmml")
model = Eps::Model.load_pmml(pmml)

A few notes:

  • The target can be numeric (regression) or categorical (classification)
  • Pass an array of hashes to predict to make multiple predictions at once
  • Models are stored in PMML, a standard for model storage

Building Models


Often, the goal of building a model is to make good predictions on future data. To help achieve this, Eps splits the data into training and validation sets if you have 30+ data points. It uses the training set to build the model and the validation set to evaluate the performance.

If your data has a time associated with it, it’s highly recommended to use that field for the split.

Eps::Model.new(data, target: :price, split: :listed_at)

Otherwise, the split is random. There are a number of other options as well.

Performance is reported in the summary.

  • For regression, it reports validation RMSE (root mean squared error) - lower is better
  • For classification, it reports validation accuracy - higher is better

Typically, the best way to improve performance is feature engineering.

Feature Engineering

Features are extremely important for model performance. Features can be:

  1. numeric
  2. categorical
  3. text


For numeric features, use any numeric type.

{bedrooms: 4, bathrooms: 2.5}


For categorical features, use strings or booleans.

{state: "CA", basement: true}

Convert any ids to strings so they’re treated as categorical features.

{city_id: city_id.to_s}

For dates, create features like day of week and month.

{weekday: sold_on.strftime("%a"), month: sold_on.strftime("%b")}

For times, create features like day of week and hour of day.

{weekday: listed_at.strftime("%a"), hour: listed_at.hour.to_s}


For text features, use strings with multiple words.

{description: "a beautiful house on top of a hill"}

This creates features based on word count (term frequency).

You can specify text features explicitly with:

Eps::Model.new(data, target: :price, text_features: [:description])

You can set advanced options with:

text_features: {
  description: {
    min_occurences: 5,
    max_features: 1000,
    min_length: 1,
    case_sensitive: true,
    tokenizer: /\s+/,
    stop_words: ["and", "the"]

Full Example

We recommend putting all the model code in a single file. This makes it easy to rebuild the model as needed.

In Rails, we recommend creating a app/ml_models directory. Be sure to restart Spring after creating the directory so files are autoloaded.

bin/spring stop

Here’s what a complete model in app/ml_models/price_model.rb may look like:

class PriceModel < Eps::Base
  def build
    houses = House.all

    # train
    data = houses.map { |v| features(v) }
    model = Eps::Model.new(data, target: :price, split: :listed_at)
    puts model.summary

    # save to file
    File.write(model_file, model.to_pmml)

    # ensure reloads from file
    @model = nil

  def predict(house)


  def features(house)
      bedrooms: house.bedrooms,
      city_id: house.city_id.to_s,
      month: house.listed_at.strftime("%b"),
      listed_at: house.listed_at,
      price: house.price

  def model
    @model ||= Eps::Model.load_pmml(File.read(model_file))

  def model_file
    File.join(__dir__, "price_model.pmml")

Build the model with:


This saves the model to price_model.pmml. Be sure to check this into source control.

Predict with:



We recommend monitoring how well your models perform over time. To do this, save your predictions to the database. Then, compare them with:

actual = houses.map(&:price)
predicted = houses.map(&:predicted_price)
Eps.metrics(actual, predicted)

For RMSE and MAE, alert if they rise above a certain threshold. For ME, alert if it moves too far away from 0. For accuracy, alert if it drops below a certain threshold.

Other Languages

Eps makes it easy to serve models from other languages. You can build models in Python, R, and others and serve them in Ruby without having to worry about how to deploy or run another language.

Eps can serve LightGBM, linear regression, and naive Bayes models. Check out ONNX Runtime and Scoruby to serve other models.


To create a model in Python, install the sklearn2pmml package

pip install sklearn2pmml

And check out the examples:


To create a model in R, install the pmml package


And check out the examples:


It’s important for features to be implemented consistently when serving models created in other languages. We highly recommend verifying this programmatically. Create a CSV file with ids and predictions from the original model.

house_id prediction
1 145000
2 123000
3 250000

Once the model is implemented in Ruby, confirm the predictions match.

model = Eps::Model.load_pmml("model.pmml")

# preload houses to prevent n+1
houses = House.all.index_by(&:id)

CSV.foreach("predictions.csv", headers: true, converters: :numeric) do |row|
  house = houses[row["house_id"]]
  expected = row["prediction"]

  actual = model.predict(bedrooms: house.bedrooms, bathrooms: house.bathrooms)

  success = actual.is_a?(String) ? actual == expected : (actual - expected).abs < 0.001
  raise "Bad prediction for house #{house.id} (exp: #{expected}, act: #{actual})" unless success

  putc ""


A number of data formats are supported. You can pass the target variable separately.

x = [{x: 1}, {x: 2}, {x: 3}]
y = [1, 2, 3]
Eps::Model.new(x, y)

Or pass arrays of arrays

x = [[1, 2], [2, 0], [3, 1]]
y = [1, 2, 3]
Eps::Model.new(x, y)


Eps works well with Daru data frames.

df = Daru::DataFrame.from_csv("houses.csv")
Eps::Model.new(df, target: "price")


When importing data from CSV files, be sure to convert numeric fields. The table method does this automatically.

CSV.table("data.csv").map { |row| row.to_h }


Pass an algorithm with:

Eps::Model.new(data, algorithm: :linear_regression)

Eps supports:

  • LightGBM (default)
  • Linear Regression
  • Naive Bayes

Linear Regression

To speed up training on large datasets with linear regression, install GSL. With Homebrew, you can use:

brew install gsl

Then, add this line to your application’s Gemfile:

gem 'gsl', group: :development

It only needs to be available in environments used to build the model.

Validation Options

Pass your own validation set with:

Eps::Model.new(data, validation_set: validation_set)

Split on a specific value

Eps::Model.new(data, split: {column: :listed_at, value: Date.parse("2019-01-01")})

Specify the validation set size (the default is 0.25, which is 25%)

Eps::Model.new(data, split: {validation_size: 0.2})

Database Storage

The database is another place you can store models. It’s good if you retrain models automatically.

We recommend adding monitoring and guardrails as well if you retrain automatically

Create an ActiveRecord model to store the predictive model.

rails g model Model key:string:uniq data:text

Store the model with:

store = Model.where(key: "price").first_or_initialize
store.update(data: model.to_pmml)

Load the model with:

data = Model.find_by!(key: "price").data
model = Eps::Model.load_pmml(data)

Jupyter & IRuby

You can use IRuby to run Eps in Jupyter notebooks. Here’s how to get IRuby working with Rails.



Eps 0.3.0 brings a number of improvements, including support for LightGBM and cross-validation. There are a number of breaking changes to be aware of:

  • LightGBM is now the default for new models. On Mac, run:
  brew install libomp

Pass the algorithm option to use linear regression or naive Bayes.

  Eps::Model.new(data, algorithm: :linear_regression) # or :naive_bayes
  • Cross-validation happens automatically by default. You no longer need to create training and test sets manually. If you were splitting on a time, use:
  Eps::Model.new(data, split: {column: :listed_at, value: Date.parse("2019-01-01")})

Or randomly, use:

  Eps::Model.new(data, split: {validation_size: 0.3})

To continue splitting manually, use:

  Eps::Model.new(data, validation_set: test_set)
  • It’s no longer possible to load models in JSON or PFA formats. Retrain models and save them as PMML.


Eps 0.2.0 brings a number of improvements, including support for classification.

We recommend:

  1. Changing Eps::Regressor to Eps::Model
  2. Converting models from JSON to PMML
  model = Eps::Model.load_json("model.json")
  File.write("model.pmml", model.to_pmml)
  1. Renaming app/stats_models to app/ml_models


View the changelog


Everyone is encouraged to help improve this project. Here are a few ways you can help:

To get started with development and testing:

git clone https://github.com/ankane/eps.git
cd eps
bundle install
rake test