Write a function called divide_dataset_into_training_and_testing as shown below that aims to split the dataset into training and testing. It must contain arugment called training_size that meet following:¶If training_size is float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train examples.

Answered: If training_size is float, should be…

Database System Concepts

7th Edition

ISBN:9780078022159

Author:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Publisher:Abraham Silberschatz Professor, Henry F. Korth, S. Sudarshan

Chapter1: Introduction

Section: Chapter Questions

Problem 1PE

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Write a function called divide_dataset_into_training_and_testing as shown below that aims to split the dataset into training and testing. It must contain arugment called training_size that meet following:¶

If training_size is float, should be between 0.0 and 1.0 and represent the proportion of the dataset to include in the train split. If int, represents the absolute number of train examples.

Expert Solution

Step 1

Splits multi-view data into random train and test subsets. This utility wraps the train_test_split function from sklearn.model_selection for ease of use.

Parameters:	inputs (sequence of indexables) -- Allowed inputs are lists of numpy arrays, numpy arrays, lists, scipy-sparse matrices or pandas dataframes. 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. If int, represents the absolute number of test samples. If None, the value is set to the complement of the train size. If train_size is also None, it will be set to 0.25. 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. If int, represents the absolute number of train samples. If None, the value is automatically set to the complement of the test size. random_state (int or RandomState instance, default=None) -- Controls the shuffling applied to the data before applying the split. Pass an int for reproducible output across multiple function calls. shuffle (bool, default=True) -- Whether or not to shuffle the data before splitting. If shuffle=False then stratify must be None. stratify (array-like, default=None) -- If not None, data is split in a stratified fashion, using this as the class labels.
Returns:	splitting -- List containing the train-test splits of each of the inputs. If a list of arrays or 3D array is one of the inputs, train_test_split operates on each subarray and puts them together into a list of arrays or 3D array for training and one for testing.
Return type:	list, length=2*len(arrays)