diff --git a/lib/node_modules/@stdlib/ml/incr/binary-classification/lib/model.js b/lib/node_modules/@stdlib/ml/incr/binary-classification/lib/model.js
index 69f543905fd3..84de66a3f841 100644
--- a/lib/node_modules/@stdlib/ml/incr/binary-classification/lib/model.js
+++ b/lib/node_modules/@stdlib/ml/incr/binary-classification/lib/model.js
@@ -341,8 +341,8 @@ setReadOnly( Model.prototype, '_modifiedHuberLoss', function modifiedHuber( x, y
 	d = y * this._dot( x.data, x.strides[ 0 ], x.offset );
 	if ( d < -1.0 ) {
 		this._add( x, 4.0*eta*y );
-	} else {
-		this._add( x, eta*( y-(d*y) ) );
+	} else if ( d < 1.0 ) {
+		this._add( x, 2.0*eta*( y-(d*y) ) );
 	}
 	return this;
 });
diff --git a/lib/node_modules/@stdlib/ml/incr/binary-classification/test/test.model.js b/lib/node_modules/@stdlib/ml/incr/binary-classification/test/test.model.js
index 29a2bfcdb5be..2b336a846e8f 100644
--- a/lib/node_modules/@stdlib/ml/incr/binary-classification/test/test.model.js
+++ b/lib/node_modules/@stdlib/ml/incr/binary-classification/test/test.model.js
@@ -21,6 +21,8 @@
 // MODULES //
 
 var tape = require( 'tape' );
+var array = require( '@stdlib/ndarray/array' );
+var Float64Array = require( '@stdlib/array/float64' );
 var Model = require( './../lib/model.js' );
 
 
@@ -32,4 +34,41 @@ tape( 'main export is a function', function test( t ) {
 	t.end();
 });
 
-// TODO: add tests
+tape( 'the function supports modified Huber loss', function test( t ) {
+	var expected;
+	var model;
+	var opts;
+	var eta;
+	var x;
+
+	eta = 0.1;
+	opts = {
+		'lambda': 0.0,
+		'learningRate': [ 'constant', eta ],
+		'loss': 'modifiedHuber',
+		'intercept': false
+	};
+	x = array( new Float64Array( [ 1.0 ] ) );
+
+	// Case 1: `-1 <= y*f(x) < 1` (quadratically smoothed region):
+	model = new Model( 1, opts );
+	model._weights[ 0 ] = 0.3; // eslint-disable-line no-underscore-dangle
+	model.update( x, 1 );
+	expected = 0.3 + ( 2.0*eta*( 1.0-(0.3*1.0) ) );
+	t.strictEqual( model._weights[ 0 ], expected, 'returns expected value' ); // eslint-disable-line no-underscore-dangle
+
+	// Case 2: `y*f(x) >= 1`, where the gradient is zero and the weights are unchanged:
+	model = new Model( 1, opts );
+	model._weights[ 0 ] = 2.0; // eslint-disable-line no-underscore-dangle
+	model.update( x, 1 );
+	t.strictEqual( model._weights[ 0 ], 2.0, 'returns expected value' ); // eslint-disable-line no-underscore-dangle
+
+	// Case 3: `y*f(x) < -1`:
+	model = new Model( 1, opts );
+	model._weights[ 0 ] = -2.0; // eslint-disable-line no-underscore-dangle
+	model.update( x, 1 );
+	expected = -2.0 + ( 4.0*eta*1.0 );
+	t.strictEqual( model._weights[ 0 ], expected, 'returns expected value' ); // eslint-disable-line no-underscore-dangle
+
+	t.end();
+});