DecisionTree | index DecisionTree.py DecisionTree.py.html |
A Decision Tree model.
The doc is here : http://scikit-learn.org/dev/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier
--------------------------------------------------------------------------------
Sortie du script
----------------
.. runblock:: console
$ python DecisionTree.py
Résultats
---------
La soumission du résultat à Kaggle donne 76.07%.
--------------------------------------------------------------------------------
Modules | ||||||
|
Data | ||
DecisionTree = DecisionTreeClassifier(compute_importances=False...s_leaf=1, min_samples_split=1, random_state=None) Number_try = 10 Output = array([ 0., 0., 1., 1., 1., 0., 0., 0., ..., 1., 1., 0., 0., 1., 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_max_depth = 19 best_min_samples_leaf = 1 best_min_samples_split = 1 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_max_depth = xrange(1, 30) list_min_samples_leaf = xrange(1, 10) list_min_samples_split = xrange(1, 10) max_depth = 29 max_depth_quality = {1: 80.03355704697988, 2: 80.536912751677846, 3: 83.389261744966447, 4: 83.892617449664414, 5: 84.395973154362423, 6: 84.899328859060418, 7: 86.409395973154361, 8: 87.919463087248332, 9: 88.422818791946298, 10: 88.926174496644293, ...} 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) min_samples_leaf = 9 min_samples_leaf_quality = {1: 90.771812080536932, 2: 87.751677852348976, 3: 86.241610738255048, 4: 85.570469798657726, 5: 85.402684563758385, 6: 84.228187919463068, 7: 83.389261744966447, 8: 83.053691275167793, 9: 83.221476510067106} min_samples_split = 9 min_samples_split_quality = {1: 90.771812080536932, 2: 90.771812080536932, 3: 89.932885906040269, 4: 90.268456375838923, 5: 89.429530201342274, 6: 89.093959731543606, 7: 88.758389261744952, 8: 89.093959731543606, 9: 88.758389261744952} 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 = 9 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 = 0.67 proportion_women = 0.35241301907968575 proportion_women_survived = 0.7420382165605095 quality = [83.22147651006712, 83.22147651006712, 83.22147651006712, 83.22147651006712, 83.22147651006712, 83.22147651006712, 83.22147651006712, 83.22147651006712, 83.22147651006712, 83.22147651006712] 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 = 98.204264870931539 sex = 3 sibsp = 5 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 = [90.771812080536932, 87.751677852348976, 86.241610738255048, 85.570469798657726, 85.402684563758385, 84.228187919463068, 83.389261744966447, 83.053691275167793, 83.221476510067106] 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) |