NoSplit

class mastml.data_splitters.NoSplit(**kwargs)

Bases: BaseSplitter

Class to just train the model on the training data and test it on that same data. Sometimes referred to as a “Full fit” or a “Single fit”, equivalent to just plotting y vs. x.

Args:

None (only object instance)

Attributes:

parallel_run: an attribute definining wheteher to run splits with all available computer cores

Methods:

get_n_splits: method to calculate the number of splits to perform

Args:

None

Returns:

(int), always 1 as only a single split is performed

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

Args:

X: (numpy array), array of X features

Returns:

(numpy array), array of train and test indices (all data used as train and test for NoSplit)

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)

Returns the number of splitting iterations in the cross-validator

split(X, y=None, groups=None)

Generate indices to split data into training and test set.

Parameters

Xarray-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.

yarray-like of shape (n_samples,)

The target variable for supervised learning problems.

groupsarray-like of shape (n_samples,), default=None

Group labels for the samples used while splitting the dataset into train/test set.

Yields

trainndarray

The training set indices for that split.

testndarray

The testing set indices for that split.