Add efficacy metrics description and pre-content for synthetic data reports

Author: devhelpr

src/assets/synthetic-data.tsx

@@ -386,6 +386,10 @@ def run():
                 'reportType': 'table',
                 'titleKey': 'syntheticData.efficacyMetricsTitle',
                 'showIndex' : False,
+                'preContent' : [{
+                    'contentType': 'text',
+                    'textKey': 'syntheticData.efficacyMetricsDescription'
+                }],
                 'data': metrics_df.to_json(orient="records"),                
             },
             {

src/locales/en.ts

@@ -188,6 +188,16 @@ For categorical (or boolean) columns the following metrics are computed:
         correlationMatrixDescription: `The matrix below illustrates the differences in pairwise correlations between variables in the original and synthetic data. 
 Green cells signify that the pairwise correlation was accurately captured, with 0 representing the best possible score. Red cells indicate poor capture of the pairwise correlation.`,
         efficacyMetricsTitle: 'Efficacy metrics',
+        efficacyMetricsDescription: `Efficacy metrics comparing real and synthetic datasets for downstream predictive tasks. The idea is to train a predictive model on synthetic data and evaluate its performance on real data. The type of metrics computed depends on the task:
+
+For regression (when the target is numerical):
+- Mean Squared Error (MSE)
+- Mean Absolute Error (MAE)
+- R^2 Score
+
+For classification (when the target is categorical/boolean):
+- Accuracy Score
+- Weighted F1 Score`,
         disclosureProtectionTitle: 'Privacy metrics',
         disclosureProtectionDescription: `A class to compute the disclosure protection metric for synthetic data. This metric measures the proportion of synthetic records that are too similar (within a defined threshold) to real records, posing a disclosure risk.`,
         outputDataTitle: '5. Generated synthetic data',