Representation Module

A representation tool for Titanic dataset.

The doc is here : http://scikit-learn.org/dev/modules/classes.html

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Je veux essayer d’utiliser les algorithmiques de clustering, de changement de représentations etc...

Sortie du script

$ python Representation.py
Opening the file 'train.csv' and 'test.csv'...
chi2:
	For the attribute pclass	, chi2=30.8736994366	, and pval=2.75378563203e-08.
	For the attribute sex	, chi2=92.7024469789	, and pval=6.07783826353e-22.
	For the attribute age	, chi2=0.308599072344	, and pval=0.578541125456.
	For the attribute sibsp	, chi2=2.58186537899	, and pval=0.108094210127.
	For the attribute parch	, chi2=10.0974991118	, and pval=0.00148470675869.
	For the attribute fare	, chi2=8.81917152221	, and pval=0.00298081971968.
	For the attribute embarked	, chi2=4.16460364538	, and pval=0.0412770695637.
f_regression:
	For the attribute pclass	, F=284.129532979	, and pval=1.57151971664e-55.
	For the attribute sex	, F=56.9535623581	, and pval=1.1039238328e-13.
	For the attribute age	, F=376.857543848	, and pval=2.92484272456e-70.
	For the attribute sibsp	, F=54.6074317492	, and pval=3.39321085969e-13.
	For the attribute parch	, F=103.639800789	, and pval=4.23490268583e-23.
	For the attribute fare	, F=306.822356392	, and pval=3.05741568504e-59.
	For the attribute embarked	, F=278.555265732	, and pval=1.31527257165e-54.
f_classif:
	For the attribute pclass	, F=115.031272188	, and pval=2.53704738798e-25.
	For the attribute sex	, F=372.405723602	, and pval=1.40606613088e-69.
	For the attribute age	, F=4.35351608908	, and pval=0.0372170837268.
	For the attribute sibsp	, F=1.11057220411	, and pval=0.292243928698.
	For the attribute parch	, F=5.9634638366	, and pval=0.0147992453747.
	For the attribute fare	, F=63.030764228	, and pval=6.12018934192e-15.
	For the attribute embarked	, F=14.3305250028	, and pval=0.000163650058954.

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