LeaveOutPercent¶

class mastml.data_splitters.LeaveOutPercent(percent_leave_out=0.2, n_repeats=5)[source]¶

Class to train the model using a certain percentage of data as training data

Args:

percent_leave_out (float): fraction of data to use in training (must be > 0 and < 1)

n_repeats (int): number of repeated splits to perform (must be >= 1)

Methods:

get_n_splits: method to return the number of splits to perform

Args:: groups: (numpy array), array of group labels
Returns:: (int), number of unique groups, indicating number of splits to perform

split: method to perform split into train indices and test indices

Args:

X: (numpy array), array of X features

y: (numpy array), array of y data

groups: (numpy array), array of group labels

Returns:

(numpy array), array of train and test indices

Methods Summary

`get_n_splits`([X, y, groups])	Returns the number of splitting iterations in the cross-validator
`split`(X[, y, groups])	Generate indices to split data into training and test set.

Methods Documentation

get_n_splits(X=None, y=None, groups=None)[source]¶: Returns the number of splitting iterations in the cross-validator

split(X, y=None, groups=None)[source]¶

Generate indices to split data into training and test set.

X : array-like of shape (n_samples, n_features): Training data, where n_samples is the number of samples and n_features is the number of features.
y : array-like of shape (n_samples,): The target variable for supervised learning problems.
groups : array-like of shape (n_samples,), default=None: Group labels for the samples used while splitting the dataset into train/test set.