Train/Test Split in sklearn

import sys
sys.path.append("C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/tools/")
sys.path.append('C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/choose_your_own')
sys.path.append('C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/datasets_questions')

import os
os.chdir('C:/Users/Jeff/udacity/Intro_to_Machine_Learning/ud120-projects/validation')

from sklearn import datasets
from sklearn.svm import SVC
from sklearn import cross_validation

iris = datasets.load_iris()
features = iris.data
labels = iris.target

### set the random_state to 0 and the test_size to 0.4 so
### we can exactly check your result
features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(iris.data, iris.target, test_size=0.4, random_state=0)


clf = SVC(kernel="linear", C=1.)
clf.fit(features_train, labels_train)

print clf.score(features_test, labels_test)

0.966666666667

Your First (Overfit) POI Identifier

import pickle
from feature_format import featureFormat, targetFeatureSplit

data_dict = pickle.load(open("../final_project/final_project_dataset.pkl", "r") )

### first element is our labels, any added elements are predictor
### features. Keep this the same for the mini-project, but you'll
### have a different feature list when you do the final project.
features_list = ["poi", "salary"]

data = featureFormat(data_dict, features_list)
labels, features = targetFeatureSplit(data)


### it's all yours from here forward!  
from sklearn.tree import DecisionTreeClassifier

clf = DecisionTreeClassifier()
clf.fit(features,labels)

clf.score(features,labels)

0.98947368421052628

Deploying a Training/Testing Regime

features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(features, labels, test_size=0.3, random_state=42)

clf.fit(features_train, labels_train)
clf.score(features_test, labels_test)

0.72413793103448276