README.md in eps-0.3.6 vs README.md in eps-0.3.7
- old
+ new
@@ -5,11 +5,11 @@
- Build predictive models quickly and easily
- Serve models built in Ruby, Python, R, and more
Check out [this post](https://ankane.org/rails-meet-data-science) for more info on machine learning with Rails
-[](https://travis-ci.org/ankane/eps)
+[](https://github.com/ankane/eps/actions)
## Installation
Add this line to your application’s Gemfile:
@@ -132,11 +132,11 @@
```ruby
{description: "a beautiful house on top of a hill"}
```
-This creates features based on word count (term frequency).
+This creates features based on [word count](https://en.wikipedia.org/wiki/Bag-of-words_model).
You can specify text features explicitly with:
```ruby
Eps::Model.new(data, target: :price, text_features: [:description])
@@ -145,16 +145,16 @@
You can set advanced options with:
```ruby
text_features: {
description: {
- min_occurences: 5,
- max_features: 1000,
- min_length: 1,
- case_sensitive: true,
- tokenizer: /\s+/,
- stop_words: ["and", "the"]
+ min_occurences: 5, # min times a word must appear to be included in the model
+ max_features: 1000, # max number of words to include in the model
+ min_length: 1, # min length of words to be included
+ case_sensitive: true, # how to treat words with different case
+ tokenizer: /\s+/, # how to tokenize the text, defaults to whitespace
+ stop_words: ["and", "the"] # words to exclude from the model
}
}
```
## Full Example
@@ -216,10 +216,10 @@
```ruby
PriceModel.build
```
-This saves the model to `price_model.pmml`. Be sure to check this into source control.
+This saves the model to `price_model.pmml`. Check this into source control or use a tool like [Trove](https://github.com/ankane/trove) to store it.
Predict with:
```ruby
PriceModel.predict(house)