8000 MAINT Fixing Typos (#11017) · scikit-learn/scikit-learn@3e26fc6 · GitHub
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MAINT Fixing Typos (#11017)
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‎benchmarks/bench_plot_randomized_svd.py

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@@ -31,7 +31,7 @@
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goal: study whether the rank of the matrix and the number of components
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extracted by randomized SVD affect "the optimal" number of power iterations
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(c) plot: time vs norm, varing datasets
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(c) plot: time vs norm, varying datasets
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data: many datasets
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goal: compare default configurations
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‎doc/developers/maintainer.rst

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@@ -84,5 +84,5 @@ The definition of what gets run in the Cron job is done in the .travis.yml
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config file, exactly the same way as the other Travis jobs. We use a ``if: type
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= cron`` filter in order for the build to be run only in Cron jobs.
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The branch targetted by the Cron job and the frequency of the Cron job is set
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The branch targeted by the Cron job and the frequency of the Cron job is set
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via the web UI at https://www.travis-ci.org/scikit-learn/scikit-learn/settings.

‎doc/glossary.rst

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:term:`outputs`, each one a finite floating point number, for a
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fixed int ``n_outputs > 1`` in a particular dataset.
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Continous multioutput targets are represented as multiple
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Continuous multioutput targets are represented as multiple
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:term:`continuous` targets, horizontally stacked into an array
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of shape ``(n_samples, n_outputs)``.
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‎doc/themes/scikit-learn/static/nature.css_t

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@@ -786,7 +786,7 @@ div.warning-wrapper p {
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}
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/*-----------------------The Code Sprint Sponser Banner---------------------*/
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/*-----------------------The Code Sprint Sponsor Banner---------------------*/
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div.sprint-wrapper {
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font-weight: bold;

‎doc/tutorial/machine_learning_map/ML_MAPS_README.txt

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@@ -19,7 +19,7 @@ so I'll try to make it as simple as possible.
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Use a Graphics editor like Inkscape Vector Graphics Editor
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to open the ml_map.svg file, in this folder. From there
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you can move objects around, ect. as you need.
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you can move objects around, etc. as you need.
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Save when done, and make sure to export a .PNG file
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to replace the old-outdated ml_map.png, as that file

‎doc/tutorial/machine_learning_map/pyparsing.py

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@@ -1604,7 +1604,7 @@ def parseString( self, instring, parseAll=False ):
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(see L{I{parseWithTabs}<parseWithTabs>})
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- define your parse action using the full C{(s,loc,toks)} signature, and
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reference the input string using the parse action's C{s} argument
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- explictly expand the tabs in your input string before calling
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- explicitly expand the tabs in your input string before calling
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C{parseString}
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Example::

‎doc/whats_new/v0.19.rst

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@@ -807,7 +807,7 @@ Trees and ensembles
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- All tree based estimators now accept a ``min_impurity_decrease``
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parameter in lieu of the ``min_impurity_split``, which is now deprecated.
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The ``min_impurity_decrease`` helps stop splitting the nodes in which
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the weighted impurity decrease from splitting is no longer alteast
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the weighted impurity decrease from splitting is no longer atleast
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``min_impurity_decrease``. :issue:`8449` by `Raghav RV`_.
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Linear, kernelized and related models

‎examples/gaussian_process/plot_gpc_isoprobability.py

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# Observations
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y = np.array(g(X) > 0, dtype=int)
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# Instanciate and fit Gaussian Process Model
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# Instantiate and fit Gaussian Process Model
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kernel = C(0.1, (1e-5, np.inf)) * DotProduct(sigma_0=0.1) ** 2
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gp = GaussianProcessClassifier(kernel=kernel)
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gp.fit(X, y)

‎examples/gaussian_process/plot_gpr_noisy_targets.py

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# its MSE
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x = np.atleast_2d(np.linspace(0, 10, 1000)).T
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# Instanciate a Gaussian Process model
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# Instantiate a Gaussian Process model
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kernel = C(1.0, (1e-3, 1e3)) * RBF(10, (1e-2, 1e2))
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gp = GaussianProcessRegressor(kernel=kernel, n_restarts_optimizer=9)
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‎examples/preprocessing/plot_scaling_importance.py

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pca = unscaled_clf.named_steps['pca']
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pca_std = std_clf.named_steps['pca']
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# Show first principal componenets
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# Show first principal components
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print('\nPC 1 without scaling:\n', pca.components_[0])
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print('\nPC 1 with scaling:\n', pca_std.components_[0])
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