added comments for interpretation of validation

ChatAttachmentAnalysisWithValidation.py

@@ -37,3 +37,18 @@ test_preds = rfr.predict(X_test)
 test_mse = mean_squared_error(y_test, test_preds)
 test_mae = mean_absolute_error(y_test, test_preds)
 print(f"Test MSE: {test_mse:.2f}, Test MAE: {test_mae:.2f}")
+
+# The validation set is used during the model building process to assess how well the model is performing.
+# It helps tune the model's hyperparameters, prevent overfitting and select the best performing model.
+# A lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) on the validation set indicate a better fit of the model.
+# These metrics measure the difference between the predicted and actual values.
+# Validation MSE: The average of the squares of the differences between the predicted and actual values in the validation set.
+# Validation MAE: The average of the absolute differences between the predicted and actual values in the validation set.
+
+# Once we are confident about our model's parameters and performance, we test it on unseen data - the test set.
+# The test set provides the final measure of the model's performance.
+# It helps us understand how the model will generalize to new, unseen data.
+# A lower Mean Squared Error (MSE) and Mean Absolute Error (MAE) on the test set also indicate a better fit of the model.
+# Test MSE: The average of the squares of the differences between the predicted and actual values in the test set.
+# Test MAE: The average of the absolute differences between the predicted and actual values in the test set.
+# Note that if the model's performance on the test set is significantly worse than on the training set, it may be an indication of overfitting.