API replace mean_squared_error(square=False) by root_mean_squared_error (scikit-learn#26734)

Co-authored-by: Alejandro Martin <alejandro.martingil@tno.nl>
Co-authored-by: jeremie du boisberranger <jeremiedbb@yahoo.fr>
Co-authored-by: Jérémie du Boisberranger <34657725+jeremiedbb@users.noreply.github.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

Author: 4 people

doc/modules/classes.rst

@@ -995,6 +995,8 @@ details.
    metrics.median_absolute_error
    metrics.mean_absolute_percentage_error
    metrics.r2_score
+   metrics.root_mean_squared_log_error
+   metrics.root_mean_squared_error
    metrics.mean_poisson_deviance
    metrics.mean_gamma_deviance
    metrics.mean_tweedie_deviance

doc/modules/model_evaluation.rst

@@ -94,8 +94,9 @@ Scoring                                Function
 'max_error'                            :func:`metrics.max_error`
 'neg_mean_absolute_error'              :func:`metrics.mean_absolute_error`
 'neg_mean_squared_error'               :func:`metrics.mean_squared_error`
-'neg_root_mean_squared_error'          :func:`metrics.mean_squared_error`
+'neg_root_mean_squared_error'          :func:`metrics.root_mean_squared_error`
 'neg_mean_squared_log_error'           :func:`metrics.mean_squared_log_error`
+'neg_root_mean_squared_log_error'      :func:`metrics.root_mean_squared_log_error`
 'neg_median_absolute_error'            :func:`metrics.median_absolute_error`
 'r2'                                   :func:`metrics.r2_score`
 'neg_mean_poisson_deviance'            :func:`metrics.mean_poisson_deviance`
@@ -2310,6 +2311,10 @@ function::
     for an example of mean squared error usage to
     evaluate gradient boosting regression.
 
+Taking the square root of the MSE, called the root mean squared error (RMSE), is another
+common metric that provides a measure in the same units as the target variable. RSME is
+available through the :func:`root_mean_squared_error` function.
+
 .. _mean_squared_log_error:
 
 Mean squared logarithmic error
@@ -2347,6 +2352,9 @@ function::
   >>> mean_squared_log_error(y_true, y_pred)
   0.044...
 
+The root mean squared logarithmic error (RMSLE) is available through the
+:func:`root_mean_squared_log_error` function.
+
 .. _mean_absolute_percentage_error:
 
 Mean absolute percentage error

doc/whats_new/v1.4.rst

@@ -238,6 +238,18 @@ Changelog
 - |Enhancement| :class:`preprocessing.TargetEncoder` now supports `target_type`
   'multiclass'. :pr:`26674` by :user:`Lucy Liu <lucyleeow>`.
 
+:mod:`sklearn.metrics`
+......................
+
+- |API| The `squared` parameter of :func:`metrics.mean_squared_error` and
+  :func:`metrics.mean_squared_log_error` is deprecated and will be removed in 1.6.
+  Use the new functions :func:`metrics.root_mean_squared_error` and
+  :func:`root_mean_squared_log_error` instead.
+  :pr:`26734` by :user:`Alejandro Martin Gil <101AlexMartin>`.
+
+- |Enhancement| Added `neg_root_mean_squared_log_error_scorer` as scorer
+  :pr:`26734` by :user:`Alejandro Martin Gil <101AlexMartin>`.
+
 :mod:`sklearn.model_selection`
 ..............................
 

sklearn/metrics/__init__.py

@@ -62,6 +62,8 @@
     mean_tweedie_deviance,
     median_absolute_error,
     r2_score,
+    root_mean_squared_error,
+    root_mean_squared_log_error,
 )
 from ._scorer import check_scoring, get_scorer, get_scorer_names, make_scorer
 from .cluster import (
@@ -166,6 +168,8 @@
     "RocCurveDisplay",
     "roc_auc_score",
     "roc_curve",
+    "root_mean_squared_log_error",
+    "root_mean_squared_error",
     "get_scorer_names",
     "silhouette_samples",
     "silhouette_score",