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
       
csv
numpy
pylab

 
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)