From 27bbdb570bac062c71b3bb21b0876fd78adc9f7e Mon Sep 17 00:00:00 2001
From: Taehoon Lee <me@taehoonlee.com>
Date: Wed, 12 Jul 2017 17:05:21 +0900
Subject: [PATCH] Fix typos (#9320)
---
examples/calibration/plot_compare_calibration.py | 6 +++---
sklearn/externals/joblib/_parallel_backends.py | 2 +-
sklearn/externals/joblib/parallel.py | 2 +-
sklearn/utils/testing.py | 2 +-
sklearn/utils/validation.py | 2 +-
5 files changed, 7 insertions(+), 7 deletions(-)
diff --git a/examples/calibration/plot_compare_calibration.py b/examples/calibration/plot_compare_calibration.py
index d935bce4f5..bc1f73a06e 100644
--- a/examples/calibration/plot_compare_calibration.py
+++ b/examples/calibration/plot_compare_calibration.py
@@ -33,9 +33,9 @@ with different biases per method:
moving the average prediction of the bagged ensemble away from 0. We observe
this effect most strongly with random forests because the base-level trees
trained with random forests have relatively high variance due to feature
- subseting." As a result, the calibration curve shows a characteristic sigmoid
- shape, indicating that the classifier could trust its "intuition" more and
- return probabilities closer to 0 or 1 typically.
+ subsetting." As a result, the calibration curve shows a characteristic
+ sigmoid shape, indicating that the classifier could trust its "intuition"
+ more and return probabilities closer to 0 or 1 typically.
* Support Vector Classification (SVC) shows an even more sigmoid curve as
the RandomForestClassifier, which is typical for maximum-margin methods
diff --git a/sklearn/externals/joblib/_parallel_backends.py b/sklearn/externals/joblib/_parallel_backends.py
index 8f3e768abd..7035f66e38 100644
--- a/sklearn/externals/joblib/_parallel_backends.py
+++ b/sklearn/externals/joblib/_parallel_backends.py
@@ -88,7 +88,7 @@ class ParallelBackendBase(with_metaclass(ABCMeta)):
managed by the backend it-self: if we expect no new tasks, there is no
point in re-creating a new working pool.
"""
- # Does nothing by default: to be overriden in subclasses when canceling
+ # Does nothing by default: to be overridden in subclasses when canceling
# tasks is possible.
pass
diff --git a/sklearn/externals/joblib/parallel.py b/sklearn/externals/joblib/parallel.py
index 73e681b870..345697e062 100644
--- a/sklearn/externals/joblib/parallel.py
+++ b/sklearn/externals/joblib/parallel.py
@@ -48,7 +48,7 @@ BACKENDS = {
DEFAULT_BACKEND = 'multiprocessing'
DEFAULT_N_JOBS = 1
-# Thread local value that can be overriden by the ``parallel_backend`` context
+# Thread local value that can be overridden by the ``parallel_backend`` context
# manager
_backend = threading.local()
diff --git a/sklearn/utils/testing.py b/sklearn/utils/testing.py
index cfaefc88d2..e308a2a7b3 100644
--- a/sklearn/utils/testing.py
+++ b/sklearn/utils/testing.py
@@ -268,7 +268,7 @@ class _IgnoreWarnings(object):
Parameters
----------
- category : tuple of warning class, defaut to Warning
+ category : tuple of warning class, default to Warning
The category to filter. By default, all the categories will be muted.
"""
diff --git a/sklearn/utils/validation.py b/sklearn/utils/validation.py
index e6e98f45ae..460f20673f 100644
--- a/sklearn/utils/validation.py
+++ b/sklearn/utils/validation.py
@@ -618,7 +618,7 @@ def has_fit_parameter(estimator, parameter):
Returns
-------
is_parameter: bool
- Whether the parameter was found to be a a named parameter of the
+ Whether the parameter was found to be a named parameter of the
estimator's fit method.
Examples
--
GitLab