README.md in isotree-0.1.2 vs README.md in isotree-0.1.3

@@ -2,10 +2,12 @@
 
 :evergreen_tree: [IsoTree](https://github.com/david-cortes/isotree) - outlier/anomaly detection using Isolation Forest - for Ruby
 
 Learn how [Isolation Forest](https://www.youtube.com/watch?v=RyFQXQf4w4w) works
 
+:deciduous_tree: Check out [OutlierTree](https://github.com/ankane/outliertree) for human-readable explanations of outliers
+
 [![Build Status](https://travis-ci.org/ankane/isotree.svg?branch=master)](https://travis-ci.org/ankane/isotree)
 
 ## Installation
 
 Add this line to your application’s Gemfile:
@@ -17,24 +19,28 @@
 ## Getting Started
 
 Prep your data
 
 ```ruby
-x = [[1, 2], [3, 4], [5, 6], [7, 8]]
+data = [
+  {department: "Books",  sale: false, price: 2.50},
+  {department: "Books",  sale: true,  price: 3.00},
+  {department: "Movies", sale: false, price: 5.00}
+]
 ```
 
 Train a model
 
 ```ruby
 model = IsoTree::IsolationForest.new
-model.fit(x)
+model.fit(data)
 ```
 
 Get outlier scores
 
 ```ruby
-model.predict(x)
+model.predict(data)
 ```
 
 Scores are between 0 and 1, with higher scores indicating outliers
 
 ## Parameters
@@ -65,16 +71,26 @@
 
 See a [detailed explanation](https://isotree.readthedocs.io/en/latest/#isotree.IsolationForest)
 
 ## Data
 
-Data can be an array of arrays
+Data can be an array of hashes
 
 ```ruby
-[[1, 2, 3], [4, 5, 6]]
+[
+  {department: "Books",  sale: false, price: 2.50},
+  {department: "Books",  sale: true,  price: 3.00},
+  {department: "Movies", sale: false, price: 5.00}
+]
 ```
 
+Or a Rover data frame
+
+```ruby
+Rover.read_csv("data.csv")
+```
+
 Or a Numo array
 
 ```ruby
 Numo::NArray.cast([[1, 2, 3], [4, 5, 6]])
 ```
@@ -90,9 +106,17 @@
 Then reinstall the gem.
 
 ```sh
 gem uninstall isotree --force
 bundle install
+```
+
+## Reference
+
+Get the average isolation depth
+
+```ruby
+model.predict(data, output: "avg_depth")
 ```
 
 ## History
 
 View the [changelog](https://github.com/ankane/isotree/blob/master/CHANGELOG.md)