Documentation for classification metrics improved (#155)

CHANGELOG.md

@@ -1,5 +1,8 @@
 # Changelog
 
+## 15.2.4
+- Documentation for classification metrics improved
+
 ## 15.2.3
 - Documentation for RMSE metric improved
 

lib/src/metric/metric_type.dart

@@ -1,5 +1,7 @@
 /// Metrics for measuring the quality of the prediction.
 enum MetricType {
+  ///
+  ///
   /// Mean percentage absolute error (MAPE), a regression metric. The formula
   /// is:
   ///
@@ -17,6 +19,8 @@ enum MetricType {
   /// can produce scores which are greater than 1.
   mape,
 
+  ///
+  ///
   /// Root mean squared error (RMSE), a regression metric. The formula is:
   ///
   ///
@@ -31,17 +35,69 @@ enum MetricType {
   /// scores within the range [0, +Infinity]
   rmse,
 
-  /// A classification metric. The greater the score produced by the metric, the
-  /// better the prediction's quality is. The metric produces scores within the
-  /// range [0, 1]
+  ///
+  ///
+  /// A classification metric. The formula is
+  ///
+  ///
+  /// ![{\mbox{Score}}=\frac{k}{n}](https://latex.codecogs.com/gif.latex?%7B%5Cmbox%7BScore%7D%7D%3D%5Cfrac%7Bk%7D%7Bn%7D)
+  ///
+  ///
+  /// where `k` is a number of correctly predicted labels, `n` - total amount
+  /// of labels
+  ///
+  ///
+  /// The greater the score produced by the metric, the better the prediction's
+  /// quality is. The metric produces scores within the range [0, 1]
   accuracy,
 
-  /// A classification metric. The greater the score produced by the metric, the
+  ///
+  ///
+  /// A classification metric. The formula for a single-class problem is
+  ///
+  ///
+  /// ![{\mbox{Score}}=\frac{TP}{TP + FP}](https://latex.codecogs.com/gif.latex?%7B%5Cmbox%7BScore%7D%7D%3D%5Cfrac%7BTP%7D%7BTP%20+%20FP%7D)
+  ///
+  ///
+  /// where TP is a number of correctly predicted positive labels (true positive),
+  /// FP - a number of incorrectly predicted positive labels (false positive). In
+  /// other words, TP + FP is a number of all the labels predicted to be positive
+  ///
+  /// The formula for a multi-class problem is
+  ///
+  ///
+  /// ![{\mbox{Score}}= \frac{1}{n}\sum_{t=1}^{n}Score_{t}](https://latex.codecogs.com/gif.latex?%7B%5Cmbox%7BScore%7D%7D%3D%20%5Cfrac%7B1%7D%7Bn%7D%5Csum_%7Bt%3D1%7D%5E%7Bn%7DScore_%7Bt%7D)
+  ///
+  /// Where `Score 1..t` are scores for each class from 1 to t
+  ///
+  ///
+  /// The greater the score produced by the metric, the
   /// better the prediction's quality is. The metric produces scores within the
   /// range [0, 1]
   precision,
 
-  /// A classification metric. The greater the score produced by the metric, the
+  ///
+  ///
+  /// A classification metric. The formula for a single-class problem is
+  ///
+  ///
+  /// ![{\mbox{Score}}=\frac{TP}{TP + FN}](https://latex.codecogs.com/gif.latex?%7B%5Cmbox%7BScore%7D%7D%3D%5Cfrac%7BTP%7D%7BTP%20+%20FN%7D)
+  ///
+  ///
+  /// where TP is a number of correctly predicted positive labels (true positive),
+  /// FN - a number of incorrectly predicted negative labels (false negative). In
+  /// other words, TP + FN is a total amount of positive labels for a class in
+  /// the given data
+  ///
+  /// The formula for a multi-class problem is
+  ///
+  ///
+  /// ![{\mbox{Score}}= \frac{1}{n}\sum_{t=1}^{n}Score_{t}](https://latex.codecogs.com/gif.latex?%7B%5Cmbox%7BScore%7D%7D%3D%20%5Cfrac%7B1%7D%7Bn%7D%5Csum_%7Bt%3D1%7D%5E%7Bn%7DScore_%7Bt%7D)
+  ///
+  ///
+  /// Where `Score 1..t` are scores for each class from 1 to t
+  ///
+  /// The greater the score produced by the metric, the
   /// better the prediction's quality is. The metric produces scores within the
   /// range [0, 1]
   recall,

pubspec.yaml

@@ -1,6 +1,6 @@
 name: ml_algo
 description: Machine learning algorithms, Machine learning models performance evaluation functionality
-version: 15.2.3
+version: 15.2.4
 homepage: https://github.com/gyrdym/ml_algo
 
 environment: