Merge pull request #35 from xiaodaigh/development

temporarily adding MLJJLBoost

Project.toml

@@ -1,12 +1,13 @@
 name = "JLBoost"
 uuid = "13d6d4a1-5e7f-472c-9ebc-8123a4fbb95f"
 authors = ["Dai ZJ <[email protected]>"]
-version = "0.1.2"
+version = "0.1.3"
 
 [deps]
 CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 LossFunctions = "30fc2ffe-d236-52d8-8643-a9d8f7c094a7"
+MLJJLBoost = "cb937e20-20f2-4cea-8a28-54eef8bab285"
 MappedArrays = "dbb5928d-eab1-5f90-85c2-b9b0edb7c900"
 Missings = "e1d29d7a-bbdc-5cf2-9ac0-f12de2c33e28"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
@@ -27,6 +28,7 @@ Tables = "0.2"
 julia = "1"
 
 [extras]
+MLJJLBoost = "cb937e20-20f2-4cea-8a28-54eef8bab285"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 Weave = "44d3d7a6-8a23-5bf8-98c5-b353f8df5ec9"
 

README.jmd

@@ -79,7 +79,7 @@ new_tree = 0.3 * trees(xgtreemodel)[1] # weight the first tree by 30%
 unique(predict(new_tree, iris) ./ predict(trees(xgtreemodel)[1], iris)) # 0.3
 ```
 
-#### MLJ.jl &
+#### MLJ.jl
 
 There is integration with the MLJ.jl modelling framework
 
@@ -105,7 +105,7 @@ One can obtain the feature importance using the `feature_importance` function
 feature_importance(xgtreemodel, iris)
 ```
 
-#### Tables.jl integrations
+#### Tables.jl integration
 
 Any Tables.jl compatible tabular data structure. So you can use any column accessible table with JLBoost. However, you are advised to define the following methods for `df` as the generic implementation in this package may not be efficient