ENH: avoid weird-looking float approximations when computing alpha(s)…

…. Clarify code.

mapie_v1/_utils.py

@@ -4,16 +4,24 @@
 from numpy import array
 from mapie._typing import ArrayLike, NDArray
 from sklearn.model_selection import BaseCrossValidator
+from decimal import Decimal
 
 
 def transform_confidence_level_to_alpha_list(
     confidence_level: Union[float, List[float]]
 ) -> List[float]:
-    if isinstance(confidence_level, float):
-        confidence_levels = [confidence_level]
-    else:
+    if isinstance(confidence_level, list):
         confidence_levels = confidence_level
-    return [1 - level for level in confidence_levels]
+    else:
+        confidence_levels = [confidence_level]
+
+    # Using decimals to avoid weird-looking float approximations
+    # when computing alpha = 1 - confidence_level
+    # Such approximations arise even with simple confidence levels like 0.9
+    confidence_levels_d = [Decimal(str(conf_level)) for conf_level in confidence_levels]
+    alphas_d = [Decimal("1") - conf_level_d for conf_level_d in confidence_levels_d]
+
+    return [float(alpha_d) for alpha_d in alphas_d]
 
 
 def check_if_param_in_allowed_values(