Improve text

Co-authored-by: AdrianSosic <[email protected]>

baybe/utils/validation.py

@@ -92,28 +92,26 @@ def validate_target_input(data: pd.DataFrame, targets: Iterable[Target]) -> None
     from baybe.targets import BinaryTarget, NumericalTarget
 
     if len(data) < 1:
-        raise ValueError("The provided input dataframe cannot be emtpy.")
+        raise ValueError("The provided input dataframe cannot be empty.")
 
     for t in targets:
         if data[t.name].isna().any():
             raise ValueError(
-                f"The target '{t.name}' has missing values or NaNs in the provided "
-                f"dataframe. Missing target values are not supported."
+                f"The target '{t.name}' has missing values in the provided dataframe."
             )
 
         if isinstance(t, NumericalTarget):
             if data[t.name].dtype.kind not in "iufb":
                 raise TypeError(
                     f"The numerical target '{t.name}' has non-numeric entries in the "
-                    f"provided dataframe. Non-numeric target values are not supported."
+                    f"provided dataframe."
                 )
         elif isinstance(t, BinaryTarget):
-            if not (
-                data[t.name].isin(allowed := [t.failure_value, t.success_value]).all()
-            ):
+            allowed = {t.failure_value, t.success_value}
+            if invalid := set(data[t.name].unique()) - allowed:
                 raise ValueError(
-                    f"The binary target '{t.name}' has nvalid entries in the provided "
-                    f"dataframe. Allowed values are: {allowed}."
+                    f"The binary target '{t.name}' has invalid entries {invalid} "
+                    f"in the provided dataframe. Allowed values are: {allowed}."
                 )
 
 
@@ -137,13 +135,13 @@ def validate_parameter_input(
         TypeError: If a parameter contains non-numeric values.
     """
     if len(data) < 1:
-        raise ValueError("The provided input dataframe cannot be emtpy.")
+        raise ValueError("The provided input dataframe cannot be empty.")
 
     for p in parameters:
         if data[p.name].isna().any():
             raise ValueError(
-                f"The parameter '{p.name}' has missing values or NaNs in the provided "
-                f"dataframe. Missing parameter values are not supported."
+                f"The parameter '{p.name}' has missing values in the provided "
+                f"dataframe."
             )
         if p.is_numerical and (data[p.name].dtype.kind not in "iufb"):
             raise TypeError(