DummyClassifier | index DummyClassifier.py DummyClassifier.py.html |
A dummy model, with 3 strategies :
* stratified;
* most_frequent;
* uniform.
The doc is here : http://scikit-learn.org/dev/modules/generated/sklearn.dummy.DummyClassifier.html#sklearn.dummy.DummyClassifier
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Sortie du script
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.. runblock:: console
$ python DummyClassifier.py
Résultats
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La soumission du résultat à Kaggle donne ??.??% (pas encore fait).
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Modules | ||||||
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Data | ||
Dummy = DummyClassifier(random_state=None, strategy='most_frequent') Number_try = 100 Output = array([ 0., 0., 0., 0., 0., 0., 0., 0., ..., 0., 0., 0., 0., 0., 0., 0., 0.]) __author__ = 'Lilian BESSON (mailto:lilian.besson[AT]normale.fr)' age = 4 age_max = 80.0 age_mean = 29.69911764705882 age_min = 0.41999999999999998 attr = 6 best_strategy = 'most_frequent' cabin = 9 csv_file_object = <_csv.reader object> data = array([['0', '3', 'Braund, Mr. Owen Harris', ......ck', ..., '7.75', '', 'Q']], dtype='|S82') data_attributes = ['survived', 'pclass', 'sex', 'age', 'sibsp', 'parch', 'fare', 'embarked'] embarked = 10 fare = 8 fare_max = 512.32920000000001 fare_mean = 32.2042079685746 fare_min = 0.0 from_c_onboard = array([ 1., 1., 1., 0., 0., 1., 0., 1., ...0., 1., 0., 1., 0., 1., 1., 1., 1., 1.]) from_q_onboard = array([ 0., 0., 1., 1., 1., 1., 0., 1., ...0., 0., 0., 0., 0., 0., 0., 1., 0., 0.]) from_s_onboard = array([ 0., 1., 1., 0., 0., 0., 1., 1., ..., 1., 0., 0., 0., 0., 0., 1., 0.]) header = ['pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked'] i = 10 known_ages = array([['0', '3', 'Braund, Mr. Owen Harris', ......ck', ..., '7.75', '', 'Q']], dtype='|S82') known_ages_died = array([['0', '3', 'Braund, Mr. Owen Harris', ......ck', ..., '7.75', '', 'Q']], dtype='|S82') known_ages_survived = array([['1', '1', 'Cumings, Mrs. John Bradley (F...', ..., '30', 'C148', 'C']], dtype='|S82') known_fares = array([['0', '3', 'Braund, Mr. Owen Harris', ......ck', ..., '7.75', '', 'Q']], dtype='|S82') known_fares_died = array([['0', '3', 'Braund, Mr. Owen Harris', ......ck', ..., '7.75', '', 'Q']], dtype='|S82') known_fares_survived = array([['1', '1', 'Cumings, Mrs. John Bradley (F...', ..., '30', 'C148', 'C']], dtype='|S82') list_strategy = ['stratified', 'most_frequent', 'uniform'] men_onboard = array([ 0., 0., 0., 0., 0., 0., 0., 0., ..., 0., 0., 0., 0., 0., 0., 1., 0.]) men_only_stats = array([ True, False, False, False, True, True,..., True, False, False, True, True], dtype=bool) name = 2 names_columns_test = ['pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked'] names_columns_train = ['survived', 'pclass', 'name', 'sex', 'age', 'sibsp', 'parch', 'ticket', 'fare', 'cabin', 'embarked'] nb_essais = 99 number_dead = 549.0 number_passengers = 891 number_survived = 342.0 open_file_object = <_csv.writer object> parch = 6 pclass = 1 proportion_c_survived = 0.5535714285714286 proportion_known_ages = 0.8013468013468014 proportion_known_fares = 1.0 proportion_men = 0.6475869809203143 proportion_men_survived = 0.18890814558058924 proportion_q_survived = 0.38961038961038963 proportion_s_survived = 0.33695652173913043 proportion_survivors = 0.38383838383838381 proportion_train = 0.75 proportion_women = 0.35241301907968575 proportion_women_survived = 0.7420382165605095 quality = [50.0, 50.1497005988024, 46.40718562874252, 48.65269461077844, 52.9940119760479, 49.40119760479042, 50.89820359281437, 52.24550898203593, 50.0, 49.10179640718563, 51.047904191616766, 51.047904191616766, 52.69461077844311, 51.34730538922156, 51.796407185628745, 47.75449101796407, 48.952095808383234, 47.604790419161674, 53.74251497005988, 50.449101796407184, ...] row = [0, '3', 'Peter, Master. Michael J', 'male', '', '1', '1', '2668', '22.3583', '', 'C'] row_dict = {1: 3, 3: 1, 4: 0.37123897058823524, 5: 1, 6: 1, 8: 0.04364049521284361, 10: 0} score = 61.616161616161612 sex = 3 sibsp = 5 strategy = 'uniform' strategy_quality = {'most_frequent': 61.976047904191567, 'stratified': 52.438622754491035, 'uniform': 50.414670658682645} survived = 0 table_embarked = {'C': 0, 'Q': 2, 'S': 1} table_sex = {'female': 0, 'male': 1} test_data = array([[ 3. , 1. , 0.43125 , .... 1. , 0.0436405 , 0. ]]) test_file_object = <_csv.reader object> ticket = 7 train_data = array([[ 1. , 1. , 1. , .... 1. , 0.04113566, 1. ]]) val = [61.976047904191567, 52.438622754491035, 50.414670658682645] women_onboard = array([ 1., 1., 1., 1., 1., 1., 1., 0., ..., 1., 1., 1., 1., 0., 0., 1., 0.]) women_only_stats = array([False, True, True, True, False, False,..., False, True, True, False, False], dtype=bool) z = 418 |
Author | ||
Lilian BESSON (mailto:lilian.besson[AT]normale.fr) |