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plot_kmeans_digits.py
3f}s\t{:.0f}\t{:.3f}\t{:.3f}\t{:.3...centroids[:, 1], marker="x", s=169, linewidths=3, color="w",...scikit-learn.org/stable/_downloads/5a87b25ba023ee709595b8d02049f021/plot_kmeans_digits.py -
plot_kmeans_digits.ipynb
3f}s\\t{:.0f}\\t{:.3f}\\t{:.3f}\\t...centroids[:, 1],\n marker=\"x\",\n s=169,\n linewidths=3,\n color=\"w\",\n...scikit-learn.org/stable/_downloads/6bf322ce1724c13e6e0f8f719ebd253c/plot_kmeans_digits.ipynb -
plot_discretization_strategies.py
set_title("strategy='%s'" % (strategy,), size=14) i +=...scikit-learn.org/stable/_downloads/43e84df0b93ff974da370e8da900f2ee/plot_discretization_strategie... -
plot_discretization_strategies.ipynb
set_title(\"strategy='%s'\" % (strategy,), size=14)\n\n...scikit-learn.org/stable/_downloads/adc9be3b7acc279025dad9ee4ce92038/plot_discretization_strategie... -
pygments.css
s { color: #4070a0 } /* Literal.String...scikit-learn.org/stable/_static/pygments.css -
plot_discretization_strategies.rst.txt
set_title("strategy='%s'" % (strategy,), size=14) i +=...scikit-learn.org/stable/_sources/auto_examples/preprocessing/plot_discretization_strategies.rst.txt -
plot_multi_metric_evaluation.ipynb
sample_score_mean = results[\"mean_%s_%s\" % (sample, scorer)]\n sample_score_std...sample_score_std = results[\"std_%s_%s\" % (sample, scorer)]\n ax.fill_between(\n...scikit-learn.org/stable/_downloads/f57e1ee55d4c7a51949d5c26b3af07bb/plot_multi_metric_evaluation.... -
plot_multi_metric_evaluation.py
sample_score_mean = results["mean_%s_%s" % (sample, scorer)] sample_score_std...sample_score_std = results["std_%s_%s" % (sample, scorer)] ax.fill_between(...scikit-learn.org/stable/_downloads/dedbcc9464f3269f4f012f4bfc7d16da/plot_multi_metric_evaluation.py -
SpotPass Archival Project
data for their game(s) is preserved before it's too late. If you...this database from your system(s), you can help various network...spotpassarchive.github.io/ -
plot_multi_metric_evaluation.rst.txt
sample_score_mean = results["mean_%s_%s" % (sample, scorer)] sample_score_std...sample_score_std = results["std_%s_%s" % (sample, scorer)] ax.fill_between(...scikit-learn.org/stable/_sources/auto_examples/model_selection/plot_multi_metric_evaluation.rst.txt