8000 MAINT Fix several typos in src and doc files · scikit-learn/scikit-learn@31ca34e · GitHub
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MAINT Fix several typos in src and doc files
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‎doc/computing/computational_performance.rst

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@@ -195,7 +195,7 @@ support vectors.
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.. centered:: |nusvr_model_complexity|
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For :mod:`sklearn.ensemble` of trees (e.g. RandomForest, GBT,
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ExtraTrees etc) the number of trees and their depth play the most
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ExtraTrees, etc.) the number of trees and their depth play the most
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important role. Latency and throughput should scale linearly with the number
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of trees. In this case we used directly the ``n_estimators`` parameter of
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:class:`~ensemble.GradientBoostingRegressor`.

‎doc/developers/contributing.rst

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@@ -548,8 +548,8 @@ message, the following actions are taken.
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[cd build gh] CD is run only for GitHub Actions
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[cd build cirrus] CD is run only for Cirrus CI
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[lint skip] Azure pipeline skips linting
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[scipy-dev] Build & test with our dependencies (numpy, scipy, etc ...) development builds
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[nogil] Build & test with the nogil experimental branches of CPython, Cython, NumPy, SciPy...
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[scipy-dev] Build & test with our dependencies (numpy, scipy, etc.) development builds
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[nogil] Build & test with the nogil experimental branches of CPython, Cython, NumPy, SciPy, ...
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[pypy] Build & test with PyPy
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[azure parallel] Run Azure CI jobs in parallel
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[float32] Run float32 tests by setting `SKLEARN_RUN_FLOAT32_TESTS=1`. See :ref:`environment_variable` for more details

‎doc/getting_started.rst

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@@ -37,8 +37,8 @@ The :term:`fit` method generally accepts 2 inputs:
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represented as rows and features are represented as columns.
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- The target values :term:`y` which are real numbers for regression tasks, or
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integers for classification (or any other discrete set of values). For
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unsupervized learning tasks, ``y`` does not need to be specified. ``y`` is
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usually 1d array where the ``i`` th entry corresponds to the target of the
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unsupervised learning tasks, ``y`` does not need to be specified. ``y`` is
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usually a 1d array where the ``i`` th entry corresponds to the target of the
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``i`` th sample (row) of ``X``.
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Both ``X`` and ``y`` are usually expected to be numpy arrays or equivalent

‎doc/modules/cross_decomposition.rst

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@@ -28,7 +28,7 @@ PLS draws similarities with `Principal Component Regression
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<https://en.wikipedia.org/wiki/Principal_component_regression>`_ (PCR), where
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the samples are first projected into a lower-dimensional subspace, and the
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targets `y` are predicted using `transformed(X)`. One issue with PCR is that
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the dimensionality reduction is unsupervized, and may lose some important
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the dimensionality reduction is unsupervised, and may lose some important
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variables: PCR would keep the features with the most variance, but it's
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possible that features with a small variances are relevant from predicting
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the target. In a way, PLS allows for the same kind of dimensionality

‎doc/modules/feature_extraction.rst

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@@ -846,7 +846,7 @@ Note that the dimensionality does not affect the CPU training time of
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algorithms which operate on CSR matrices (``LinearSVC(dual=True)``,
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``Perceptron``, ``SGDClassifier``, ``PassiveAggressive``) but it does for
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algorithms that work with CSC matrices (``LinearSVC(dual=False)``, ``Lasso()``,
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etc).
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etc.).
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Let's try again with the default setting::
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‎doc/modules/lda_qda.rst

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@@ -137,7 +137,7 @@ Mathematical formulation of LDA dimensionality reduction
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First note that the K means :math:`\mu_k` are vectors in
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:math:`\mathcal{R}^d`, and they lie in an affine subspace :math:`H` of
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dimension at most :math:`K - 1` (2 points lie on a line, 3 points lie on a
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plane, etc).
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plane, etc.).
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As mentioned above, we can interpret LDA as assigning :math:`x` to the class
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whose mean :math:`\mu_k` is the closest in terms of Mahalanobis distance,

‎sklearn/ensemble/_hist_gradient_boosting/splitting.pyx

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@@ -499,9 +499,9 @@ cdef class Splitter:
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split_infos[split_info_idx].feature_idx = feature_idx
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# For each feature, find best bin to split on
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# Start with a gain of -1 (if no better split is found, that
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# Start with a gain of -1 if no better split is found, that
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# means one of the constraints isn't respected
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# (min_samples_leaf, etc) and the grower will later turn the
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# (min_samples_leaf, etc.) and the grower will later turn the
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# node into a leaf.
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split_infos[split_info_idx].gain = -1
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split_infos[split_info_idx].is_categorical = is_categorical[feature_idx]

‎sklearn/metrics/_classification.py

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@@ -316,7 +316,7 @@ def confusion_matrix(
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[0, 0, 1],
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[1, 0, 2]])
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In the binary case, we can extract true positives, etc as follows:
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In the binary case, we can extract true positives, etc. as follows:
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>>> tn, fp, fn, tp = confusion_matrix([0, 1, 0, 1], [1, 1, 1, 0]).ravel()
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>>> (tn, fp, fn, tp)

‎sklearn/model_selection/tests/test_search.py

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@@ -379,7 +379,7 @@ def test_no_refit():
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and hasattr(grid_search, "best_params_")
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)
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# Make sure the functions predict/transform etc raise meaningful
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# Make sure the functions predict/transform etc. raise meaningful
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# error messages
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for fn_name in (
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"predict",

‎sklearn/neural_network/_multilayer_perceptron.py

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@@ -360,7 +360,7 @@ def _backprop(self, X, y, activations, deltas, coef_grads, intercept_grads):
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return loss, coef_grads, intercept_grads
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def _initialize(self, y, layer_units, dtype):
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# set all attributes, allocate weights etc for first call
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# set all attributes, allocate weights etc. for first call
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# Initialize parameters
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self.n_iter_ = 0
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self.t_ = 0

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