DecisionTreeClassifier: Proofreading the documentation (#221)

Author: gyrdym

CHANGELOG.md

@@ -1,5 +1,9 @@
 # Changelog
 
+## 16.10.4
+- DecisionTreeClassifier:
+    - Proofreading the documentation
+
 ## 16.10.3
 - CrossValidator:
     - Proofreading the documentation

lib/src/classifier/decision_tree_classifier/decision_tree_classifier.dart

@@ -12,14 +12,14 @@ import 'package:ml_dataframe/ml_dataframe.dart';
 import 'package:ml_linalg/dtype.dart';
 
 /// A class that performs decision tree-based classification
-///
-/// Decision tree is an algorithm that recursively splits the input data into
-/// subsets until the subsets conforming certain stop criteria are met.
-///
-/// Process of forming such a recursive subsets structure is called
-/// decision tree learning. Once a decision tree learned, it may be used to
-/// classify new samples with the same features that were used to learn the
-/// tree.
+//
+// The decision tree is an algorithm that recursively splits the input data
+// into subsets until certain stop criteria are met - e.g. a tree node contains
+// a certain number of observations, or the tree depth is equal to a given value
+//
+// The process of forming such a data structure is called decision tree
+// learning. Once a decision tree is built, it may be used to classify new
+// samples with the same features that were used to learn the tree.
 abstract class DecisionTreeClassifier
     implements
         Assessable,
@@ -28,27 +28,25 @@ abstract class DecisionTreeClassifier
         Classifier {
   /// Parameters:
   ///
-  /// [trainData] A [DataFrame] with observations that will be used by the
-  /// classifier to learn a decision tree. Must contain [targetName] column.
+  /// [trainData] A [DataFrame] with observations that will be used to build a
+  /// decision tree. Must contain [targetName] column.
   ///
-  /// [targetName] A name of a column in [trainData] that contains class
-  /// labels
+  /// [targetName] A name of a column in [trainData] that contains class labels
   ///
-  /// [minError] A value within the range 0..1 (both inclusive). The value
-  /// denotes a minimal error on a single decision tree node and is used as a
-  /// stop criteria to avoid farther decision's tree node splitting: if the
-  /// node is good enough, there is no need to split it and thus it will become
-  /// a leaf.
+  /// [minError] A value within the range 0..1 (both inclusive). The value is a
+  /// minimal error on a single decision tree node and is used as a stop
+  /// criterion to avoid further decision tree node splitting: if the node is
+  /// good enough, there is no need to split it and thus it will become a leaf.
   ///
   /// [minSamplesCount] A minimal number of samples (observations) on the
   /// decision's tree node. The value is used as a stop criteria to avoid
-  /// farther decision's tree node splitting: if the node contains less than or
+  /// further decision tree node splitting: if the node contains less than or
   /// equal to [minSamplesCount] observations, the node turns into the leaf.
   ///
   /// [maxDepth] A maximum number of decision tree levels.
   ///
-  /// [assessorType] Defines an assessment type that will be applied to a subset
-  /// of data in order to decide how to split the subset while building the tree.
+  /// [assessorType] Defines an assessment type that will be applied to the
+  /// data in order to decide how to split the subset while building the tree.
   /// Default value is [TreeAssessorType.gini]
   ///
   /// Possible values of [assessorType] :
@@ -109,16 +107,15 @@ abstract class DecisionTreeClassifier
   /// final json = await file.readAsString();
   /// final restoredClassifier = DecisionTreeClassifier.fromJson(json);
   ///
-  /// // here you can use previously fitted restored classifier to make
-  /// // some prediction, e.g. via `DecisionTreeClassifier.predict(...)`;
+  /// // here you can do whatever you want with the restored classifier
   /// ````
   factory DecisionTreeClassifier.fromJson(String json) =>
       initDecisionTreeModule()
           .get<DecisionTreeClassifierFactory>()
           .fromJson(json);
 
   /// A minimal error on a single decision tree node. It is used as a
-  /// stop criteria to avoid farther decision's tree node splitting: if the
+  /// stop criteria to avoid further decision tree node splitting: if the
   /// node is good enough, there is no need to split it and thus it can be
   /// considered a leaf.
   ///
@@ -129,7 +126,7 @@ abstract class DecisionTreeClassifier
 
   /// A minimal number of samples (observations) on the
   /// decision's tree node. The value is used as a stop criteria to avoid
-  /// farther decision's tree node splitting: if the node contains less than or
+  /// further decision tree node splitting: if the node contains less than or
   /// equal to [minSamplesCount] observations, the node is considered a leaf.
   ///
   /// The value is read-only, it's a hyperparameter of the model
@@ -140,11 +137,11 @@ abstract class DecisionTreeClassifier
   /// The value is read-only, it's a hyperparameter of the model
   int get maxDepth;
 
-  /// An assessment type that was applied to a subset of data in order to
-  /// decide how to split the subset while building the tree
+  /// An assessment type that was applied to the initial data in order to
+  /// decide how to split it while building the tree
   TreeAssessorType get assessorType;
 
-  /// Saves tree as SVG-image. Example:
+  /// Saves the tree as an SVG-image. Example:
   ///
   /// ```dart
   /// final samples = (await fromCsv('path/to/dataset.csv'));

pubspec.yaml

@@ -1,6 +1,6 @@
 name: ml_algo
 description: Machine learning algorithms, Machine learning models performance evaluation functionality
-version: 16.10.3
+version: 16.10.4
 homepage: https://github.com/gyrdym/ml_algo
 
 environment: