stats and validation doc update

Author: KshitizLohia

ads/feature_store/docs/source/feature_validation.rst

@@ -4,7 +4,7 @@ Feature Validation
 *************
 
 Feature validation is the process of checking the quality and accuracy of the features used in a machine learning model. This is important because features that are not accurate or reliable can lead to poor model performance.
-Feature store allows you to define expectation on the data which is being materialized into feature group & dataset. This is achieved using open source library Great Expectations.
+Feature store allows you to define expectation on the data which is being materialized into feature group and dataset. This is achieved using open source library Great Expectations.
 
 .. note::
   `Great Expectations <https://docs.greatexpectations.io/docs/>`_ is a Python-based open-source library for validating, documenting, and profiling your data. It helps you to maintain data quality and improve communication about data between teams. Software developers have long known that automated testing is essential for managing complex codebases.
@@ -51,4 +51,4 @@ Expectation Suite is a collection of verifiable assertions i.e. expectations abo
 
     # Create an Expectation Suite
     suite = context.add_expectation_suite(expectation_suite_name="example_suite")
-    suite.add_expectation(expect_config)
+    suite.add_expectation(expect_config)

ads/feature_store/docs/source/statistics.rst

@@ -12,32 +12,40 @@ to derive insights about the data quality. These statistical metrics are compute
 
 The statistical metrics that are computed by feature store depend on the feature type.
 
-Metrics for categorical data
-
-- Count
-- TopKFrequentElements
-- TypeMetric
-- DuplicateCount
-- Mode
-- DistinctCount
-
-Metrics for numerical data
-
-- Skewness
-- StandardDeviation
-- Min
-- IsConstantFeature
-- IQR
-- Range
-- ProbabilityDistribution
-- Variance
-- TypeMetric
-- FrequencyDistribution
-- Count
-- Max
-- DistinctCount
-- Sum
-- IsQuasiConstantFeature
-- Quartiles
-- Mean
-- Kurtosis
++------------------------+-----------------------+
+| Numerical Metrics      | Categorical Metrics   |
++========================+=======================+
+| Skewness               | Count                 |
++------------------------+-----------------------+
+| StandardDeviation      | TopKFrequentElements  |
++------------------------+-----------------------+
+| Min                    | TypeMetric            |
++------------------------+-----------------------+
+| IsConstantFeature      | DuplicateCount        |
++------------------------+-----------------------+
+| IQR                    | Mode                  |
++------------------------+-----------------------+
+| Range                  | DistinctCount         |
++------------------------+-----------------------+
+| ProbabilityDistribution|                       |
++------------------------+-----------------------+
+| Variance               |                       |
++------------------------+-----------------------+
+| FrequencyDistribution  |                       |
++------------------------+-----------------------+
+| Count                  |                       |
++------------------------+-----------------------+
+| Max                    |                       |
++------------------------+-----------------------+
+| DistinctCount          |                       |
++------------------------+-----------------------+
+| Sum                    |                       |
++------------------------+-----------------------+
+| IsQuasiConstantFeature |                       |
++------------------------+-----------------------+
+| Quartiles              |                       |
++------------------------+-----------------------+
+| Mean                   |                       |
++------------------------+-----------------------+
+| Kurtosis               |                       |
++------------------------+-----------------------+