@@ -145,7 +145,7 @@ class BalancingLearner(BaseLearner):

             learner.remove_unfinished()

     @classmethod

-    def from_combos(cls, f, learner_type, learner_kwargs, combos):

+    def from_product(cls, f, learner_type, learner_kwargs, combos):

         """Create a `BalancingLearner` with learners of all combinations of

         named variables’ values.

@@ -176,7 +176,7 @@ class BalancingLearner(BaseLearner):

         ...     'beta': np.linspace(0, 1, 5),

         ... }

-        >>> learner = BalancingLearner.from_combos(

+        >>> learner = BalancingLearner.from_product(

         ...     f, Learner1D, dict(bounds=(0, 1)), combos)

         Notes

@@ -645,7 +645,7 @@

    "cell_type": "markdown",

    "metadata": {},

    "source": [

-    "Often one wants to create a set of `learner`s for a cartesian product of parameters. For that particular case we've added a `classmethod` called `from_combos`. See how it works below"

+    "Often one wants to create a set of `learner`s for a cartesian product of parameters. For that particular case we've added a `classmethod` called `from_product`. See how it works below"

    ]

   },

   {

@@ -664,7 +664,7 @@

     "    'beta': np.linspace(0, 1, 5),\n",

     "}\n",

     "\n",

-    "learner = adaptive.BalancingLearner.from_combos(\n",

+    "learner = adaptive.BalancingLearner.from_product(\n",

     "    jacobi, adaptive.Learner1D, dict(bounds=(0, 1)), combos)\n",

     "\n",

     "runner = adaptive.BlockingRunner(learner, goal=lambda l: l.loss() < 0.01)\n",