diff --git a/phygnn/model_interfaces/base_model.py b/phygnn/model_interfaces/base_model.py
index 6990e87..2317c0f 100644
--- a/phygnn/model_interfaces/base_model.py
+++ b/phygnn/model_interfaces/base_model.py
@@ -2,6 +2,7 @@
 """
 Base Model Interface
 """
+from abc import ABC
 import logging
 import numpy as np
 import pandas as pd
@@ -12,7 +13,7 @@
 logger = logging.getLogger(__name__)
 
 
-class ModelBase:
+class ModelBase(ABC):
     """
     Base Model Interface
     """
@@ -381,6 +382,28 @@ def _parse_data(data, names=None):
 
         return data, names
 
+    @staticmethod
+    def _get_item_number(arr):
+        """
+        Get number of items in array (labels or features)
+
+        Parameters
+        ----------
+        arr : ndarray
+            1 or 2D array
+
+        Returns
+        -------
+        n : int
+            Number of items
+        """
+        if len(arr.shape) == 1:
+            n = 1
+        else:
+            n = arr.shape[1]
+
+        return n
+
     def get_norm_params(self, names):
         """
         Get means and stdevs for given feature/label names
@@ -527,7 +550,8 @@ def _normalize_arr(self, arr, names):
         norm_arr : ndarray
             Normalized features/label
         """
-        if len(names) != arr.shape[1]:
+        n_names = self._get_item_number(arr)
+        if len(names) != n_names:
             msg = ("Number of item names ({}) does not match number of items "
                    "({})".format(len(names), arr.shape[1]))
             logger.error(msg)
@@ -647,7 +671,8 @@ def _unnormalize_arr(self, arr, names):
         native_arr : ndarray
             Native features/label array
         """
-        if len(names) != arr.shape[1]:
+        n_names = self._get_item_number(arr)
+        if len(names) != n_names:
             msg = ("Number of item names ({}) does not match number of items "
                    "({})".format(len(names), arr.shape[1]))
             logger.error(msg)
@@ -789,11 +814,7 @@ def _parse_labels(self, labels, names=None, normalize=True):
         labels, label_names = self._parse_data(labels, names=names)
 
         if self.label_names is not None:
-            if len(labels.shape) == 1:
-                n_labels = len(labels)
-            else:
-                n_labels = labels.shape[1]
-
+            n_labels = self._get_item_number(labels)
             if n_labels != len(self.label_names):
                 msg = ('data has {} labels but expected {}'
                        .format(labels.shape[1], self.label_dims))
diff --git a/phygnn/model_interfaces/random_forest_model.py b/phygnn/model_interfaces/random_forest_model.py
index 5a948e8..d9ff7a2 100644
--- a/phygnn/model_interfaces/random_forest_model.py
+++ b/phygnn/model_interfaces/random_forest_model.py
@@ -33,7 +33,7 @@ def __init__(self, model, feature_names=None, label_name=None,
             parameters (mean, stdev), by default None
         """
         super().__init__(model, feature_names=feature_names,
-                         label_name=label_name, norm_params=norm_params)
+                         label_names=label_name, norm_params=norm_params)
 
         if len(self.label_names) > 1:
             msg = ("Only a single label can be supplied to {}, but {} were"
@@ -133,8 +133,7 @@ def train_model(self, features, label, norm_label=True, parse_kwargs=None,
 
         features = self._parse_features(features, **parse_kwargs)
 
-        label = self._parse_data(label, normalize=norm_label,
-                                 names=True)
+        label = self._parse_labels(label, normalize=norm_label)
 
         if fit_kwargs is None:
             fit_kwargs = {}
@@ -204,10 +203,10 @@ def train(cls, features, label, norm_label=True, save_path=None,
             compile_kwargs = {}
 
         _, feature_names = cls._parse_data(features)
-        _, label_names = cls._parse_data(label)
+        _, label_name = cls._parse_data(label)
 
         model = cls(cls.compile_model(**compile_kwargs),
-                    feature_names=feature_names, label_names=label_names)
+                    feature_names=feature_names, label_name=label_name)
 
         model.train_model(features, label, norm_label=norm_label,
                           parse_kwargs=parse_kwargs, fit_kwargs=fit_kwargs)
diff --git a/phygnn/version.py b/phygnn/version.py
index db781ae..eebca35 100644
--- a/phygnn/version.py
+++ b/phygnn/version.py
@@ -1,4 +1,4 @@
 # -*- coding: utf-8 -*-
 """Physics Guided Neural Network version."""
 
-__version__ = '0.0.0'
+__version__ = '0.0.1'
diff --git a/tests/test_random_forest_model.py b/tests/test_random_forest_model.py
new file mode 100644
index 0000000..85256fb
--- /dev/null
+++ b/tests/test_random_forest_model.py
@@ -0,0 +1,31 @@
+"""
+Tests for basic phygnn functionality and execution.
+"""
+# pylint: disable=W0613
+import numpy as np
+import pandas as pd
+
+from phygnn.model_interfaces.random_forest_model import RandomForestModel
+
+
+N = 100
+A = np.linspace(-1, 1, N)
+B = np.linspace(-1, 1, N)
+A, B = np.meshgrid(A, B)
+A = np.expand_dims(A.flatten(), axis=1)
+B = np.expand_dims(B.flatten(), axis=1)
+
+Y = np.sqrt(A ** 2 + B ** 2)
+X = np.hstack((A, B))
+features = pd.DataFrame(X, columns=['a', 'b'])
+
+Y_NOISE = Y * (1 + (np.random.random(Y.shape) - 0.5) * 0.5) + 0.1
+labels = pd.DataFrame(Y_NOISE, columns=['c'])
+
+
+def test_random_forest():
+    """Test the RandomForestModel """
+    model = RandomForestModel.train(features, labels)
+
+    test_mae = np.mean(np.abs(model[X].values.ravel() - Y))
+    assert test_mae < 0.4