rename section to "Thresholding Probabilistic Predictors"

docs/make.jl

@@ -67,7 +67,7 @@ pages = [
     "Learning Curves" => "learning_curves.md",
     "Preparing Data" => "preparing_data.md",
     "Transformers and Other Unsupervised models" => "transformers.md",
-    "More on Probabilistic Predictors" => "more_on_probabilistic_predictors.md",
+    "Thresholding Probabilistic Predictors" => "thresholding_probabilistic_predictors.md",
     "Composing Models" => "composing_models.md",
     "Linear Pipelines" => "linear_pipelines.md",
     "Target Transformations" => "target_transformations.md",

docs/src/index.md

@@ -56,18 +56,19 @@ To support MLJ development, please cite these works or star the repo:
 [Model Search](@ref model_search) |
 [Loading Model Code](@ref) |
 [Transformers and Other Unsupervised Models](@ref) |
-[More on Probabilistic Predictors](@ref) |
-[Composing Models](@ref) |
 [Simple User Defined Models](@ref) |
 [List of Supported Models](@ref model_list) |
 [Third Party Packages](@ref) 
 
 ### Meta-algorithms
 [Evaluating Model Performance](@ref) |
 [Tuning Models](@ref) |
+[Composing Models](@ref) |
 [Controlling Iterative Models](@ref) |
 [Learning Curves](@ref)|
-[Correcting Class Imbalance](@ref)
+[Correcting Class Imbalance](@ref) |
+[Thresholding Probabilistic Predictors](@ref)
+
 
 ### Composition
 [Composing Models](@ref) |

docs/src/more_on_probabilistic_predictors.md → docs/src/thresholding_probabilistic_predictors.md

@@ -1,4 +1,4 @@
-# More on Probabilistic Predictors
+# Thresholding Probabilistic Predictors
 
 Although one can call `predict_mode` on a probabilistic binary
 classifier to get deterministic predictions, a more flexible strategy

src/MLJ.jl

@@ -17,7 +17,7 @@ following model wrappers: `Pipeline`, `TunedModel`, `EnsembleModel`, `IteratedMo
   `evaluate`/`evaluate!` for model performance, `|>` pipeline syntax,
   `TransformedTargetModel` wrapper, general model composition syntax (learning networks),
   synthetic data generators, `scitype` and `schema` methods (from ScientificTypes.jl) for
-  checking how MLJ interprets your data
+  checking how MLJ interprets your data. Generally required for any MLJ workflow.
 
 - StatisticalMeasures.jl: MLJ-compatible measures (metrics) for machine learning,
   confusion matrices, ROC curves.