diff --git a/dataframe-learn.cabal b/dataframe-learn.cabal
--- a/dataframe-learn.cabal
+++ b/dataframe-learn.cabal
@@ -1,6 +1,6 @@
 cabal-version:      2.4
 name:               dataframe-learn
-version:            1.1.0.0
+version:            1.1.0.1
 synopsis:           Interpretable, expression-returning machine learning for the dataframe ecosystem.
 description:
     A small scikit-learn-style ML library where every model returns both an
@@ -82,7 +82,7 @@
                         random >= 1.2 && < 2,
                         dataframe-core ^>= 1.1,
                         dataframe-operations ^>= 1.1.1,
-                        text >= 2.0 && < 3,
+                        text >= 2.1 && < 3,
                         vector ^>= 0.13,
                         vector-algorithms ^>= 0.9
     hs-source-dirs:     src
diff --git a/src/DataFrame/Featurize/Internal.hs b/src/DataFrame/Featurize/Internal.hs
--- a/src/DataFrame/Featurize/Internal.hs
+++ b/src/DataFrame/Featurize/Internal.hs
@@ -31,6 +31,7 @@
 import qualified Data.Vector as V
 import qualified Data.Vector.Unboxed as VU
 
+import DataFrame.Errors (DataFrameException (..), TypeErrorContext (..))
 import qualified DataFrame.Functions as F
 import DataFrame.Internal.Column (Columnable)
 import DataFrame.Internal.DataFrame (DataFrame, columnNames)
@@ -53,8 +54,19 @@
     colMajor = V.fromList (map column names)
     column name = case columnAsDoubleVector (F.col @Double name) df of
         Right v -> v
-        Left e -> throw e
+        Left e -> throw (asFeatureError name e)
 
+asFeatureError :: T.Text -> DataFrameException -> DataFrameException
+asFeatureError name (TypeMismatchException ctx) =
+    TypeMismatchException
+        ctx
+            { errorColumnName = Just (T.unpack name)
+            , callingFunctionName =
+                Just
+                    "model fit (feature columns must be numeric Double; drop or encode non-numeric columns)"
+            }
+asFeatureError _ e = e
+
 -- | The target column as a vector of doubles.
 targetDoubles :: Expr Double -> DataFrame -> VU.Vector Double
 targetDoubles expr df = case columnAsDoubleVector expr df of
@@ -85,7 +97,9 @@
   where
     names = map columnExprName features
     cols = map (materializeColumn df) features
-    n = if null cols then 0 else VU.length (head cols)
+    n = case cols of
+        (x: _) -> VU.length x
+        _      -> 0
     d = length cols
     rows = V.generate n (\i -> VU.generate d (\j -> (cols !! j) VU.! i))