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【ポテト好きは絶対やって！】「巻くだけでカリカリ！？」「冗談抜きで100個イケる」簡単すぎて手...
BuzzFeed 2人分 材料： じゃがいも（薄切り） 8枚 塩 ひとつまみ 魚肉ソーセージ’（8等分に切る） 2本 サラダ油...BuzzFeed ② 魚肉ソーセージに①を巻く。 BuzzFeed ③ フライパンにサラダ油を引き、②を中火でカリッするまで焼いたら、完成！...www.buzzfeed.com/jp/maorikato/roll-potato-with-fish-sausage -
【秒で味しみ！！】きゅうりが10本じゃ足りないってマジ！？爆速きゅうり漬けレシピ・作り方｜Tasty
BuzzFeed 2人分 材料： きゅうり（薄切り） 1本 【A】塩 小さじ1/3 【A】砂糖 小さじ1/2 わさび 小さじ1/2...小さじ1/2 作り方： ① ボウルにきゅうりと【A】を入れてラップをかけ、600Wの電子レンジで40秒加熱する。 BuzzFeed...www.buzzfeed.com/jp/maorikato/quick-pickled-cucumber -
FeatureHasher and DictVectorizer Comparison — s...
'is': 2, 'one': 2, 'example': 1, 'but': 1,...FeatureHasher is 2**20. Here we set n_features = 2**18 to illustrate...scikit-learn.org/stable/auto_examples/text/plot_hashing_vs_dict_vectorizer.html -
11. Common pitfalls and recommended practices —...
2.2. Data leakage during pre-processing..., test ) [0 3 5 6 7] [1 2 4 8 9] [1 2 4 8 9] [0 3 5 6 7] >>> for...scikit-learn.org/stable/common_pitfalls.html -
Gaussian Mixture Model Selection — scikit-learn...
2 ), C ) # general component_2 = 0.7 * np . random...scatter ( component_2 [:, 0 ], component_2 [:, 1 ], s = 0.8 )...scikit-learn.org/stable/auto_examples/mixture/plot_gmm_selection.html -
Comparing anomaly detection algorithms for outl...
make_blobs ( centers = [[ 2 , 2 ], [ - 2 , - 2 ]], cluster_std = [...make_blobs ( centers = [[ 2 , 2 ], [ - 2 , - 2 ]], cluster_std = [...scikit-learn.org/stable/auto_examples/miscellaneous/plot_anomaly_comparison.html -
MultiTaskElasticNetCV — scikit-learn 1.7.1 docu...
[ 2 , 2 ]], ... [[ 0 , 0 ], [ 1 , 1 ], [ 2 , 2 ]]) Mul...it is: 1 / ( 2 * n_samples ) * || y - Xw ||^ 2_2 + alpha * l1_ratio...scikit-learn.org/stable/modules/generated/sklearn.linear_model.MultiTaskElasticNetCV.html -
Post-tuning the decision threshold for cost-sen...
# (2) in Elkan paper [2]_. gain_matrix = np...metric" , ) axs [ 2 ] . legend () axs [ 2 ] . set_xlabel ( "Decision...scikit-learn.org/stable/auto_examples/model_selection/plot_cost_sensitive_learning.html -
RBF SVM parameters — scikit-learn 1.7.1 documen...
problem involving only 2 input features and 2 possible target classes...sub-sample the dataset to keep only 2 classes and make it a binary classification...scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html -
Face completion with a multi-output estimators ...
n_pixels // 2 :] X_test = test [:, : ( n_pixels + 1 ) // 2 ] y_test.... figure ( figsize = ( 2.0 * n_cols , 2.26 * n_faces )) plt ....scikit-learn.org/stable/auto_examples/miscellaneous/plot_multioutput_face_completion.html