diff --git a/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb b/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
index bcd01b0135a43d289ced864c785dafa952e70e01..e826ff3b4276abc72dd4a5b1faf053ab1c5e69d8 100644
--- a/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
+++ b/ProgrammingAssignment_1/ProgrammingAssignment1.ipynb
@@ -188,21 +188,19 @@
     "    Inherits Model class. Implements the k-NN algorithm for classification.\n",
     "    '''\n",
     "       \n",
-    "    def fit(self, training_features, training_labels, classes, k, distance_f,**kwargs):\n",
+    "    def fit(self, training_features, training_labels, k, distance_f,**kwargs):\n",
     "        '''\n",
     "        Fit the model. This is pretty straightforward for k-NN.\n",
     "        Args:\n",
     "            training_features: ndarray\n",
     "            training_labels: ndarray\n",
-    "            classes: ndarray\n",
-    "                1D array containing unique classes in the dataset\n",
     "            k: int\n",
     "            distance_f: function\n",
     "            kwargs: dict\n",
     "                Contains keyword arguments that will be passed to distance_f\n",
     "        '''\n",
     "        # TODO\n",
-    "        # set self.train_features, self.train_labels, self.classes, self.k, self.distance_f, self.distance_metric\n",
+    "        # set self.train_features, self.train_labels, self.k, self.distance_f, self.distance_metric\n",
     "        \n",
     "        raise NotImplementedError\n",
     "\n",
@@ -216,7 +214,7 @@
     "                mxd array containing features for the points to be predicted\n",
     "        Returns: \n",
     "            preds: ndarray\n",
-    "                mx1 array containing proportion of positive class for each test point\n",
+    "                mx1 array containing proportion of positive class among k nearest neighbors of each test point\n",
     "        '''\n",
     "        raise NotImplementedError\n",
     "        \n",