LeavePOut
Leave-P-Out cross-validator
Provides train/test indices to split data in train/test sets. This results in testing on all distinct samples of size p, while the remaining n - p samples form the training set in each iteration.
Note: LeavePOut(p) is NOT equivalent to KFold(n\_splits=n\_samples // p) which creates non-overlapping test sets.
Due to the high number of iterations which grows combinatorically with the number of samples this cross-validation method can be very costly. For large datasets one should favor KFold, StratifiedKFold or ShuffleSplit.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new LeavePOut(opts?: object): LeavePOut;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.p? | number | Size of the test sets. Must be strictly less than the number of samples. |
Returns
Defined in: generated/model_selection/LeavePOut.ts:29 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/model_selection/LeavePOut.ts:27 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/model_selection/LeavePOut.ts:26 (opens in a new tab)
_py
PythonBridge
Defined in: generated/model_selection/LeavePOut.ts:25 (opens in a new tab)
id
string
Defined in: generated/model_selection/LeavePOut.ts:22 (opens in a new tab)
opts
any
Defined in: generated/model_selection/LeavePOut.ts:23 (opens in a new tab)
Accessors
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/model_selection/LeavePOut.ts:39 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/model_selection/LeavePOut.ts:43 (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/LeavePOut.ts:89 (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<any>;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. |
opts.groups? | any | Always ignored, exists for compatibility. |
opts.y? | any | Always ignored, exists for compatibility. |
Returns
Promise<any>
Defined in: generated/model_selection/LeavePOut.ts:106 (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/LeavePOut.ts:52 (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. |
opts.groups? | ArrayLike | Group labels for the samples used while splitting the dataset into train/test set. |
opts.y? | ArrayLike | The target variable for supervised learning problems. |
Returns
Promise<ArrayLike>
Defined in: generated/model_selection/LeavePOut.ts:151 (opens in a new tab)