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RMSLE (root mean squared log error) #20326

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Jun 25, 2021
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RMSLE (root mean squared log error) #20326

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5 changes: 5 additions & 0 deletions doc/whats_new/v1.0.rst
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Expand Up @@ -583,6 +583,11 @@ Changelog
:func:`~sklearn.utils.check_array` in 1.0 and will raise a `TypeError` in
1.2. :pr:`20165` by `Thomas Fan`_.

:mod: `sklearn.metrics`
.........................
- |Feature| :func:`~sklearn.metrics.mean_square_log_error` now supports
`squared='False'`. :pr:`20322` by :user: `Uttam kumar <helper-uttam>`.
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Code and Documentation Contributors
-----------------------------------

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16 changes: 15 additions & 1 deletion sklearn/metrics/_regression.py
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Expand Up @@ -21,6 +21,7 @@
# Konstantin Shmelkov <konstantin.shmelkov@polytechnique.edu>
# Christian Lorentzen <lorentzen.ch@googlemail.com>
# Ashutosh Hathidara <ashutoshhathidara98@gmail.com>
# Uttam kumar <bajiraouttamsinha@gmail.com>
# License: BSD 3 clause

import numpy as np
Expand Down Expand Up @@ -437,7 +438,7 @@ def mean_squared_error(


def mean_squared_log_error(
y_true, y_pred, *, sample_weight=None, multioutput="uniform_average"
y_true, y_pred, *, sample_weight=None, multioutput="uniform_average", squared=True
):
"""Mean squared logarithmic error regression loss.

Expand Down Expand Up @@ -467,6 +468,10 @@ def mean_squared_log_error(
'uniform_average' :
Errors of all outputs are averaged with uniform weight.

'squared' : bool, default=True
If True returns MSLE(mean squared log error) value.
If False returns RMSLE(root mean squared log error) value.
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Returns
-------
loss : float or ndarray of floats
Expand All @@ -480,14 +485,22 @@ def mean_squared_log_error(
>>> y_pred = [2.5, 5, 4, 8]
>>> mean_squared_log_error(y_true, y_pred)
0.039...
>>> mean_squared_log_error(y_true, y_pred, squared=False)
0.199...
>>> y_true = [[0.5, 1], [1, 2], [7, 6]]
>>> y_pred = [[0.5, 2], [1, 2.5], [8, 8]]
>>> mean_squared_log_error(y_true, y_pred)
0.044...
>>> mean_squared_log_error(y_true, y_pred, squared=False)
0.210...
>>> mean_squared_log_error(y_true, y_pred, multioutput='raw_values')
array([0.00462428, 0.08377444])
>>> mean_squared_log_error(y_true, y_pred, multioutput='raw_values',squared=False)
array([0.06800207, 0.28943815]))
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>>> mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7])
0.060...
>>> mean_squared_log_error(y_true, y_pred, multioutput=[0.3, 0.7], squared=False)
0.245...
"""
y_type, y_true, y_pred, multioutput = _check_reg_targets(
y_true, y_pred, multioutput
Expand All @@ -505,6 +518,7 @@ def mean_squared_log_error(
np.log1p(y_pred),
sample_weight=sample_weight,
multioutput=multioutput,
squared=squared,
)


Expand Down