**Random Test-Train Splits** perform a random permutation of splits on your dataset converting it into training and test sets.

**Note**: Random splits do **not** guarantee that all folds will be different.

**Cross-validation** can be done with the **cross_val_score()** helper function on the estimator(**LogisticRegression**), dataset and split technique(**ShuffleSplit**)

**cross_val_score()**returns scores of the estimator for each**fold**

This **recipe** includes the following topics:

- Load data/file from github
- Split columns into the usual feature columns(X) and target column(Y)
- Set test size to 33%
- Set
**seed**to reproduce the same random data each time - Set total random
**permutation**to perform: 10 - Split data using
**ShuffleSplit()**class - Instantiate a classification model (
**LogisticRegression**) - Call
**cross_val_score()**to run cross validation - Calculate
**mean**and**standard deviation**from scores returned by**cross_val_score()**

```
# import modules
import pandas as pd
import numpy as np
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import ShuffleSplit
from sklearn.model_selection import cross_val_score
# read data file from github
# dataframe: pimaDf
gitFileURL = 'https://raw.githubusercontent.com/andrewgurung/data-repository/master/pima-indians-diabetes.data.csv'
cols = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
pimaDf = pd.read_csv(gitFileURL, names = cols)
# convert into numpy array for scikit-learn
pimaArr = pimaDf.values
# Let's split columns into the usual feature columns(X) and target column(Y)
# Y represents the target 'class' column whose value is either '0' or '1'
X = pimaArr[:, 0:8]
Y = pimaArr[:, 8]
# set test size to 33%
test_size = 0.33
# set seed to reproduce the same random data each time
seed = 7
# set total random permutation to perform
n_splits = 10
# split data using LeaveOneOut
shufflesplit = ShuffleSplit(n_splits=n_splits, test_size=test_size, random_state=seed)
# instantiate a classification model
model = LogisticRegression()
# call cross_val_score() to run cross validation
resultArr = cross_val_score(model, X, Y, cv=shufflesplit)
# calculate mean of scores for all folds
meanAccuracy = resultArr.mean() * 100
# calculate standard deviation of scores for all folds
stdAccuracy = resultArr.std() * 100
# display accuracy
print("Mean accuracy: %.3f%%, Standard deviation: %.3f%%" % (meanAccuracy, stdAccuracy))
```

```
Mean accuracy: 76.496%, Standard deviation: 1.698%
```