@@ -128,7 +128,7 @@ class Learner1D(BaseLearner):

                 pass

             if self.vector_output is None:

-                self.vector_output = hasattr(y, '__len__')

+                self.vector_output = hasattr(y, '__len__') and len(y) > 1

         else:

             # The keys of data_interp are the unknown points

@@ -234,6 +234,7 @@ class Learner1D(BaseLearner):

                                                 fill_value=0)

                 interp_ys = ip(xs_unfinished).T

             else:

+                ys = np.array(ys).flatten()  # ys could be a list of arrays with shape (1,)

                 interp_ys = np.interp(xs_unfinished, xs, ys)

         data_interp = {x: y for x, y in zip(xs_unfinished, interp_ys)}

@@ -241,18 +242,15 @@ class Learner1D(BaseLearner):

         return data_interp

     def plot(self):

+        if not self.data:

+            return hv.Scatter([]) * hv.Path([])

+

         if not self.vector_output:

-            if self.data:

-                return hv.Scatter(self.data)

-            else:

-                return hv.Scatter([])

+            return hv.Scatter(self.data) * hv.Path([])

         else:

-            if self.data:

-                xs = list(self.data.keys())

-                ys = list(self.data.values())

-                return hv.Path((xs, ys))

-            else:

-                return hv.Path([])

+            xs = list(self.data.keys())

+            ys = list(self.data.values())

+            return hv.Path((xs, ys)) * hv.Scatter([])

     def remove_unfinished(self):

         self.data_interp = {}

@@ -393,7 +393,7 @@

    "cell_type": "markdown",

    "metadata": {},

    "source": [

-    "This is again a function with sharp peaks at different x-values and with different constant backgrounds. To learn this function we can use a `Learner1D` with the argument `vector_output=True`."

+    "This is again a function with sharp peaks at different x-values and with different constant backgrounds. To learn this function we can use a `Learner1D` as well."

    ]

   },

   {

@@ -404,7 +404,7 @@

    "source": [

     "from adaptive.runner import SequentialExecutor\n",

     "\n",

-    "learner = adaptive.Learner1D(f_levels, bounds=(-1, 1), vector_output=True)\n",

+    "learner = adaptive.Learner1D(f_levels, bounds=(-1, 1))\n",

     "runner = adaptive.Runner(learner, SequentialExecutor(), goal=lambda l: l.loss() < 0.05)"

    ]

   },