Sklearn.StratifiedShuffleSplit() function in Python

28 Dec, 2021
In this article, we’ll learn about the StratifiedShuffleSplit cross validator from sklearn library which gives train-test indices to split the data into train-test sets. 
StratifiedShuffleSplit is a combination of both ShuffleSplit and StratifiedKFold. Using StratifiedShuffleSplit the proportion of distribution of class labels is almost even between train and test dataset. The major difference between StratifiedShuffleSplit and StratifiedKFold (shuffle=True) is that in StratifiedKFold, the dataset is shuffled only once in the beginning and then split into the specified number of folds. This discards any chances of overlapping of the train-test sets. However, in StratifiedShuffleSplit the data is shuffled each time before the split is done and this is why there’s a greater chance that overlapping might be possible between train-test sets. 
Syntax: sklearn.model_selection.StratifiedShuffleSplit(n_splits=10, *, test_size=None, train_size=None, random_state=None)  
Parameters:
n_splits: int, default=10
Number of re-shuffling & splitting iterations.
test_size: float or int, default=None
If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the test split. 
train_size: float or int, default=None
If float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. 
random_state: int
Controls the randomness of the training and testing indices produced. 
Below is the Implementation.
Step 1) Import required modules.
Python3
# import the librariesimport pandas as pdfrom sklearn.ensemble import RandomForestClassifierfrom sklearn import preprocessingfrom sklearn.metrics import accuracy_scorefrom sklearn.model_selection import StratifiedShuffleSplit
Step 2) Load the dataset and identify the dependent and independent variables. 
The dataset can be downloaded from here.
Python3
# convert data set into dataframechurn_df = pd.read_csv(r"ChurnData.csv") # assign dependent and independent variablesX = churn_df[['tenure', 'age', 'address', 'income',              'ed', 'employ', 'equip',   'callcard', 'wireless']] y = churn_df['churn'].astype('int')
Step 3) Pre-process data.
Python3
# data pre-processingX = preprocessing.StandardScaler().fit(X).transform(X)
Step 4) Create object of StratifiedShuffleSplit Class.
Python3
# use StratifiedShuffleSplit()sss = StratifiedShuffleSplit(n_splits=4, test_size=0.5,                             random_state=0)sss.get_n_splits(X, y)
Output:
Step 5) Call the instance and split the data frame into training sample and testing sample. The split() function returns indices for the train-test samples. Use a regression algorithm and compare accuracy fo each predicted value.
Python3
scores = [] # using regression to get predicted datarf = RandomForestClassifier(n_estimators=40, max_depth=7)for train_index, test_index in sss.split(X, y):    X_train, X_test = X[train_index], X[test_index]    y_train, y_test = y[train_index], y[test_index]    rf.fit(X_train, y_train)    pred = rf.predict(X_test)    scores.append(accuracy_score(y_test, pred)) # get accuracy of each predictionprint(scores)
Output:
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Machine Learning
Python
Machine Learning
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