From 90f0bbf3001f5149a40271305c1fa771b26e4ed7 Mon Sep 17 00:00:00 2001
From: Sebastin Santy <sebastinssanty@gmail.com>
Date: Tue, 11 Jul 2017 03:54:07 +0530
Subject: [PATCH] DOC Residual sum vs. regression sum (#9314)
---
sklearn/multioutput.py | 4 ++--
1 file changed, 2 insertions(+), 2 deletions(-)
diff --git a/sklearn/multioutput.py b/sklearn/multioutput.py
index 64e394272f..6906d95869 100644
--- a/sklearn/multioutput.py
+++ b/sklearn/multioutput.py
@@ -252,8 +252,8 @@ class MultiOutputRegressor(MultiOutputEstimator, RegressorMixin):
def score(self, X, y, sample_weight=None):
"""Returns the coefficient of determination R^2 of the prediction.
- The coefficient R^2 is defined as (1 - u/v), where u is the regression
- sum of squares ((y_true - y_pred) ** 2).sum() and v is the residual
+ The coefficient R^2 is defined as (1 - u/v), where u is the residual
+ sum of squares ((y_true - y_pred) ** 2).sum() and v is the regression
sum of squares ((y_true - y_true.mean()) ** 2).sum().
Best possible score is 1.0 and it can be negative (because the
model can be arbitrarily worse). A constant model that always
--
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