StratifiedShuffleSplit
Stratified ShuffleSplit cross-validator
Provides train/test indices to split data in train/test sets.
This cross-validation object is a merge of StratifiedKFold and ShuffleSplit, which returns stratified randomized folds. The folds are made by preserving the percentage of samples for each class.
Note: like the ShuffleSplit strategy, stratified random splits do not guarantee that all folds will be different, although this is still very likely for sizeable datasets.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new StratifiedShuffleSplit(opts?: object): StratifiedShuffleSplit;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.n_splits? | number | Number of re-shuffling & splitting iterations. Default Value 10 |
opts.random_state? | number | Controls the randomness of the training and testing indices produced. Pass an int for reproducible output across multiple function calls. See Glossary. |
opts.test_size? | number | 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 undefined, the value is set to the complement of the train size. If train\_size is also undefined, it will be set to 0.1. |
opts.train_size? | number | 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 undefined, the value is automatically set to the complement of the test size. |
Returns
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:29 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:27 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:26 (opens in a new tab)
_py
PythonBridge
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:25 (opens in a new tab)
id
string
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:22 (opens in a new tab)
opts
any
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:23 (opens in a new tab)
Accessors
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:56 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:60 (opens in a new tab)
Methods
dispose()
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Signature
dispose(): Promise<void>;Returns
Promise<void>
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:115 (opens in a new tab)
get_n_splits()
Returns the number of splitting iterations in the cross-validator
Signature
get_n_splits(opts: object): Promise<number>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | any | Always ignored, exists for compatibility. |
opts.groups? | any | Always ignored, exists for compatibility. |
opts.y? | any | Always ignored, exists for compatibility. |
Returns
Promise<number>
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:132 (opens in a new tab)
init()
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Signature
init(py: PythonBridge): Promise<void>;Parameters
| Name | Type |
|---|---|
py | PythonBridge |
Returns
Promise<void>
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:69 (opens in a new tab)
split()
Generate indices to split data into training and test set.
Signature
split(opts: object): Promise<ArrayLike>;Parameters
| Name | Type | Description |
|---|---|---|
opts | object | - |
opts.X? | ArrayLike[] | Training data, where n\_samples is the number of samples and n\_features is the number of features. Note that providing y is sufficient to generate the splits and hence np.zeros(n\_samples) may be used as a placeholder for X instead of actual training data. |
opts.groups? | any | Always ignored, exists for compatibility. |
opts.y? | ArrayLike | The target variable for supervised learning problems. Stratification is done based on the y labels. |
Returns
Promise<ArrayLike>
Defined in: generated/model_selection/StratifiedShuffleSplit.ts:179 (opens in a new tab)