README...

README.md

@@ -1,7 +1,7 @@
 ScikitLearnBase.jl
 ------------
 
-This package exposes the scikit-learn interface. Libraries that implement this
+This package exposes the scikit-learn interface. Packages that implement this
 interface can be used in conjunction with [ScikitLearn.jl](https://github.com/cstjean/ScikitLearn.jl) (pipelines, cross-validation, hyperparameter tuning, ...)
 
 This is an intentionally slim package (~100 LOC, no dependencies). That way,
@@ -13,10 +13,10 @@ Overview
 
 The docs contain [an overview of the API](http://scikitlearnjl.readthedocs.org/en/latest/api/) and a [more thorough specification](docs/API.md).
 
-There are two implementation strategies for an existing library:
+There are two implementation strategies for an existing machine learning package:
 
  - *Create a new type that wraps the existing type*. The new type can usually be written entirely on top of the existing codebase (i.e. without modifying it). This gives more implementation freedom, and a more consistent interface amongst the various ScikitLearn.jl models. Here's an [example](https://github.com/cstjean/DecisionTree.jl/blob/2722950c8f0c5e5c62204364308e28d4123383cb/src/scikitlearnAPI.jl) from DecisionTree.jl
- - *Adapt the existing type*. This requires less code, and is usually better when the model type already contains the hyperparameters / fitting arguments.
+ - *Use the existing type*. This requires less code, and is usually better when the model type already contains the hyperparameters / fitting arguments.
 
 Example
 -----
@@ -45,6 +45,7 @@ ScikitLearnBase.@declare_hyperparameters(NaiveBayes, [:bias])
 ScikitLearnBase.is_classifier(::NaiveBayes) = true   # not required for transformers
 
 function ScikitLearnBase.fit!(model::NaiveBayes, X, y)
+    # X should be of size (n_sample, n_feature)
     .... # modify model.counts here
     return model
 end
@@ -54,8 +55,7 @@ function ScikitLearnBase.predict(model::NaiveBayes, X)
 end
 ```
 
-You can try it out with `ScikitLearn.CrossValidation.cross_val_score`. Models
-with more complex hyperparameter specifications should implement `clone`,
+Models with more complex hyperparameter specifications should implement `clone`,
 `get_params` and `set_params!` explicitly instead of using
 `@declare_hyperparameters`.