Continued updates to API reference in documentation

docs/source/api_reference.rst

@@ -174,11 +174,11 @@ Classifiers are models which can be trained to predict a class label from input
     :template: estimator_class.rst
     :nosignatures:
 
-    LogisticRegressionClassifier
+    CatBoostClassifier
     RandomForestClassifier
+    LogisticRegressionClassifier
     XGBoostClassifier
 
-
 Regressors
 -----------
 
@@ -189,9 +189,10 @@ Regressors are models which can be trained to predict a target value from input
     :template: estimator_class.rst
     :nosignatures:
 
+    CatBoostRegressor
     LinearRegressor
     RandomForestRegressor
-
+    XGBoostRegressor
 
 .. currentmodule:: evalml.objectives
 

docs/source/changelog.rst

@@ -52,6 +52,7 @@ Changelog
         * Have leads example notebook load S3 files using https, so we can delete s3fs dev dependency :pr:`664`
         * Categorized components in API reference and added descriptions for each category :pr:`663`
         * Fixed Sphinx warnings about BalancedAccuracy objective :pr:`669`
+        * Updated API reference to include missing components and clean up pipeline docstrings :pr:`689`
         * Reorganize API ref, and clarify pipeline sub-titles :pr:`688`
     * Testing Changes
         * Matched install commands of `check_latest_dependencies` test and it's GitHub action :pr:`578`

evalml/pipelines/binary_classification_pipeline.py

@@ -8,7 +8,7 @@
 
 
 class BinaryClassificationPipeline(ClassificationPipeline):
-
+    """Pipeline subclass for all binary classification pipelines."""
     threshold = None
     problem_type = ProblemTypes.BINARY
 

evalml/pipelines/classification/xgboost_binary.py

@@ -2,6 +2,6 @@
 
 
 class XGBoostBinaryPipeline(BinaryClassificationPipeline):
-    """XGBoost Pipeline for both binary and multiclass classification"""
+    """XGBoost Pipeline for binary classification"""
     custom_name = "XGBoost Binary Classification Pipeline"
     component_graph = ['One Hot Encoder', 'Simple Imputer', 'RF Classifier Select From Model', 'XGBoost Classifier']

evalml/pipelines/classification_pipeline.py

@@ -6,6 +6,7 @@
 
 
 class ClassificationPipeline(PipelineBase):
+    """Pipeline subclass for all classification pipelines."""
 
     def predict_proba(self, X):
         """Make probability estimates for labels.

evalml/pipelines/multiclass_classification_pipeline.py

@@ -8,6 +8,7 @@
 
 
 class MulticlassClassificationPipeline(ClassificationPipeline):
+    """Pipeline subclass for all multiclass classification pipelines."""
     problem_type = ProblemTypes.MULTICLASS
 
     def score(self, X, y, objectives):

evalml/pipelines/regression_pipeline.py

@@ -3,4 +3,5 @@
 
 
 class RegressionPipeline(PipelineBase):
+    """Pipeline subclass for all regression pipelines."""
     problem_type = ProblemTypes.REGRESSION