updating MCM docs

NannyML · Jun 27, 2023 · f49202c · f49202c
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diff --git a/...ulticlass/tutorial-confusion-matrix-estimation-multiclass-analysis-with-ref.svg b/...ulticlass/tutorial-confusion-matrix-estimation-multiclass-analysis-with-ref.svg
diff --git a/...xample_notebooks/Tutorial - Estimating Confusion Matrix - Multiclass Classification.ipynb b/...xample_notebooks/Tutorial - Estimating Confusion Matrix - Multiclass Classification.ipynb
diff --git a/...calculation/multiclass_performance_calculation/confusion_matrix_calculation.rst b/...calculation/multiclass_performance_calculation/confusion_matrix_calculation.rst
@@ -1,8 +1,8 @@
 .. _multiclass-confusion-matrix-calculation:
 
-========================================================================================
+===================================================================
 Calculating Confusion Matrix Elements for Multiclass Classification
-========================================================================================
+===================================================================
 
 This tutorial explains how to use NannyML to calculate the :term:`confusion matrix<Confusion Matrix>` for multiclass classification
 models.

diff --git a/..._calculation/multiclass_performance_calculation/standard_metric_calculation.rst b/..._calculation/multiclass_performance_calculation/standard_metric_calculation.rst
@@ -1,8 +1,8 @@
 .. _multiclass-standard-metric-calculation:
 
-================================================================
-Monitoring Realized Performance for Multiclass Classification
-================================================================
+======================================================================
+Calculating Standard Performance Metrics for Multiclass Classification
+======================================================================
 
 .. note::
     The following example uses :term:`timestamps<Timestamp>`.

diff --git a/docs/tutorials/performance_estimation/multiclass_performance_estimation.rst b/docs/tutorials/performance_estimation/multiclass_performance_estimation.rst
@@ -1,8 +1,8 @@
 .. _multiclass-performance-estimation:
 
-================================================
+====================================================
 Estimating Performance for Multiclass Classification
-================================================
+====================================================
 
 We currently support the following **standard** metrics for multiclass classification performance estimation:
 

diff --git a/...ce_estimation/multiclass_performance_estimation/confusion_matrix_estimation.rst b/...ce_estimation/multiclass_performance_estimation/confusion_matrix_estimation.rst
@@ -1,8 +1,9 @@
 .. _multiclass-confusion-matrix-estimation:
 
-========================================================================================
+==================================================================
 Estimating Confusion Matrix Elements for Multiclass Classification
-========================================================================================
+==================================================================
+
 This tutorial explains how to use NannyML to estimate the :term:`confusion matrix<Confusion Matrix>` for multiclass classification
 models in the absence of target data. To find out how CBPE estimates performance, read the :ref:`explanation of Confidence-based
 Performance Estimation<performance-estimation-deep-dive>`.
@@ -162,8 +163,6 @@ What's next
 -----------
 
 The :ref:`Data Drift<data-drift>` functionality can help us to understand whether data drift is causing the performance problem.
-
-You can learn more about the Confidence Based Performance Estimation and its limitations in the
-:ref:`How it Works page<performance-estimation-deep-dive>`.
-
-
+When the target values become available we can use
+:ref:`realized performance calculation<multiclass-confusion-matrix-calculation>` to
+:ref:`compare realized and estimated confusion matrix results<compare_estimated_and_realized_performance>`.
diff --git a/...nce_estimation/multiclass_performance_estimation/standard_metric_estimation.rst b/...nce_estimation/multiclass_performance_estimation/standard_metric_estimation.rst
@@ -1,8 +1,8 @@
 .. _multiclass_standard-metric-estimation:
 
-====================================================
-Estimating Performance for Multiclass Classification
-====================================================
+=====================================================================
+Estimating Standard Performance Metrics for Multiclass Classification
+=====================================================================
 
 This tutorial explains how to use NannyML to estimate the performance of binary classification
 models in the absence of target data. To find out how :class:`~nannyml.performance_estimation.confidence_based.cbpe.CBPE` estimates performance, read the :ref:`explanation of Confidence-based
@@ -140,7 +140,6 @@ What's next
 -----------
 
 The :ref:`Data Drift<data-drift>` functionality can help us to understand whether data drift is causing the performance problem.
-When the target results become available they can be :ref:`compared with the estimated results<compare_estimated_and_realized_performance>`.
-
-You can learn more about the Confidence Based Performance Estimation and its limitations in the
-:ref:`How it Works page<performance-estimation-deep-dive>`.
+When the target values become available we can use
+:ref:`realized performance calculation<multiclass-standard-metric-calculation>` to
+:ref:`compare realized and estimated confusion matrix results<compare_estimated_and_realized_performance>`.