diff --git a/lib/node_modules/@stdlib/ml/base/sgd-classification/loss-functions/README.md b/lib/node_modules/@stdlib/ml/base/sgd-classification/loss-functions/README.md
index 56de67b9e878..010419e81e15 100644
--- a/lib/node_modules/@stdlib/ml/base/sgd-classification/loss-functions/README.md
+++ b/lib/node_modules/@stdlib/ml/base/sgd-classification/loss-functions/README.md
@@ -127,24 +127,24 @@ bool = isLossFunction( 'beep' );
 #include "stdlib/ml/base/sgd-classification/loss_functions.h"
 ```
 
-#### STDLIB_ML_SGD_CLASSIFICATION
+#### STDLIB_ML_SGD_CLASSIFICATION_LOSS_FUNCTION
 
 An enumeration of SGD classification loss functions with the following fields:
 
--   **STDLIB_ML_BASE_SGD_CLASSIFICATION_EPSILON_INSENSITIVE**: penalty is the absolute value of the error whenever the absolute error exceeds epsilon and zero otherwise.
--   **STDLIB_ML_BASE_SGD_CLASSIFICATION_HINGE**: corresponds to a soft-margin linear Support Vector Machine (SVM), which can handle non-linearly separable data.
--   **STDLIB_ML_BASE_SGD_CLASSIFICATION_HUBER**: squared-error loss for observations with error smaller than epsilon in magnitude, linear loss otherwise.
--   **STDLIB_ML_BASE_SGD_CLASSIFICATION_LOG**: corresponds to Logistic Regression.
--   **STDLIB_ML_BASE_SGD_CLASSIFICATION_MODIFIED_HUBER**: Huber loss function variant for classification.
--   **STDLIB_ML_BASE_SGD_CLASSIFICATION_PERCEPTRON**: corresponds to the original perceptron by Rosenblatt (1957).
--   **STDLIB_ML_BASE_SGD_CLASSIFICATION_SQUARED_EPSILON_INSENSITIVE**: squared epsilon insensitive loss function.
--   **STDLIB_ML_BASE_SGD_CLASSIFICATION_SQUARED_ERROR**: squared difference of the observed and fitted values.
--   **STDLIB_ML_BASE_SGD_CLASSIFICATION_SQUARED_HINGE**: squared hinge loss function SVM (L2-SVM).
+-   **STDLIB_ML_SGD_CLASSIFICATION_EPSILON_INSENSITIVE**: penalty is the absolute value of the error whenever the absolute error exceeds epsilon and zero otherwise.
+-   **STDLIB_ML_SGD_CLASSIFICATION_HINGE**: corresponds to a soft-margin linear Support Vector Machine (SVM), which can handle non-linearly separable data.
+-   **STDLIB_ML_SGD_CLASSIFICATION_HUBER**: squared-error loss for observations with error smaller than epsilon in magnitude, linear loss otherwise.
+-   **STDLIB_ML_SGD_CLASSIFICATION_LOG**: corresponds to Logistic Regression.
+-   **STDLIB_ML_SGD_CLASSIFICATION_MODIFIED_HUBER**: Huber loss function variant for classification.
+-   **STDLIB_ML_SGD_CLASSIFICATION_PERCEPTRON**: corresponds to the original perceptron by Rosenblatt (1957).
+-   **STDLIB_ML_SGD_CLASSIFICATION_SQUARED_EPSILON_INSENSITIVE**: squared epsilon insensitive loss function.
+-   **STDLIB_ML_SGD_CLASSIFICATION_SQUARED_ERROR**: squared difference of the observed and fitted values.
+-   **STDLIB_ML_SGD_CLASSIFICATION_SQUARED_HINGE**: squared hinge loss function SVM (L2-SVM).
 
 ```c
 #include "stdlib/ml/base/sgd-classification/loss_functions.h"
 
-const enum STDLIB_ML_SGD_CLASSIFICATION_LOSS_FUNCTIONS v = STDLIB_ML_SGD_CLASSIFICATION_HINGE;
+const enum STDLIB_ML_SGD_CLASSIFICATION_LOSS_FUNCTION v = STDLIB_ML_SGD_CLASSIFICATION_HINGE;
 ```
 
 </section>
diff --git a/lib/node_modules/@stdlib/ml/base/sgd-classification/loss-functions/include/stdlib/ml/base/sgd-classification/loss_functions.h b/lib/node_modules/@stdlib/ml/base/sgd-classification/loss-functions/include/stdlib/ml/base/sgd-classification/loss_functions.h
index 2fb7b3fc33ef..fe1820bcc94f 100644
--- a/lib/node_modules/@stdlib/ml/base/sgd-classification/loss-functions/include/stdlib/ml/base/sgd-classification/loss_functions.h
+++ b/lib/node_modules/@stdlib/ml/base/sgd-classification/loss-functions/include/stdlib/ml/base/sgd-classification/loss_functions.h
@@ -22,33 +22,33 @@
 /**
 * Enumeration of SGD classification loss functions.
 */
-enum STDLIB_ML_BASE_SGD_CLASSIFICATION_LOSS_FUNCTIONS {
+enum STDLIB_ML_SGD_CLASSIFICATION_LOSS_FUNCTION {
 	// Penalty is the absolute value of the error whenever the absolute error exceeds epsilon and zero otherwise:
-	STDLIB_ML_BASE_SGD_CLASSIFICATION_EPSILON_INSENSITIVE = 0,
+	STDLIB_ML_SGD_CLASSIFICATION_EPSILON_INSENSITIVE = 0,
 
 	// Corresponds to a soft-margin linear Support Vector Machine (SVM), which can handle non-linearly separable data:
-	STDLIB_ML_BASE_SGD_CLASSIFICATION_HINGE,
+	STDLIB_ML_SGD_CLASSIFICATION_HINGE,
 
 	// Squared-error loss for observations with error smaller than epsilon in magnitude, linear loss otherwise:
-	STDLIB_ML_BASE_SGD_CLASSIFICATION_HUBER,
+	STDLIB_ML_SGD_CLASSIFICATION_HUBER,
 
 	// Corresponds to Logistic Regression:
-	STDLIB_ML_BASE_SGD_CLASSIFICATION_LOG,
+	STDLIB_ML_SGD_CLASSIFICATION_LOG,
 
 	// Huber loss function variant for classification:
-	STDLIB_ML_BASE_SGD_CLASSIFICATION_MODIFIED_HUBER,
+	STDLIB_ML_SGD_CLASSIFICATION_MODIFIED_HUBER,
 
 	// Corresponds to the original perceptron by Rosenblatt (1957):
-	STDLIB_ML_BASE_SGD_CLASSIFICATION_PERCEPTRON,
+	STDLIB_ML_SGD_CLASSIFICATION_PERCEPTRON,
 
 	// Squared epsilon insensitive loss function:
-	STDLIB_ML_BASE_SGD_CLASSIFICATION_SQUARED_EPSILON_INSENSITIVE,
+	STDLIB_ML_SGD_CLASSIFICATION_SQUARED_EPSILON_INSENSITIVE,
 
 	// Squared difference of the observed and fitted values:
-	STDLIB_ML_BASE_SGD_CLASSIFICATION_SQUARED_ERROR,
+	STDLIB_ML_SGD_CLASSIFICATION_SQUARED_ERROR,
 
 	// Squared hinge loss function SVM (L2-SVM):
-	STDLIB_ML_BASE_SGD_CLASSIFICATION_SQUARED_HINGE
+	STDLIB_ML_SGD_CLASSIFICATION_SQUARED_HINGE
 };
 
 #endif // !STDLIB_ML_BASE_SGD_CLASSIFICATION_LOSS_FUNCTIONS_H