LeaveOneGroupOut
Leave One Group Out cross-validator
Provides train/test indices to split data such that each training set is comprised of all samples except ones belonging to one specific group. Arbitrary domain specific group information is provided an array integers that encodes the group of each sample.
For instance the groups could be the year of collection of the samples and thus allow for cross-validation against time-based splits.
Read more in the User Guide.
Python Reference (opens in a new tab)
Constructors
constructor()
Signature
new LeaveOneGroupOut(opts?: object): LeaveOneGroupOut;Parameters
| Name | Type | Description |
|---|---|---|
opts? | object | - |
opts.X? | any | Always ignored, exists for compatibility. |
opts.groups? | ArrayLike | Group labels for the samples used while splitting the dataset into train/test set. This ‘groups’ parameter must always be specified to calculate the number of splits, though the other parameters can be omitted. |
opts.y? | any | Always ignored, exists for compatibility. |
Returns
Defined in: generated/model_selection/LeaveOneGroupOut.ts:27 (opens in a new tab)
Properties
_isDisposed
boolean=false
Defined in: generated/model_selection/LeaveOneGroupOut.ts:25 (opens in a new tab)
_isInitialized
boolean=false
Defined in: generated/model_selection/LeaveOneGroupOut.ts:24 (opens in a new tab)
_py
PythonBridge
Defined in: generated/model_selection/LeaveOneGroupOut.ts:23 (opens in a new tab)
id
string
Defined in: generated/model_selection/LeaveOneGroupOut.ts:20 (opens in a new tab)
opts
any
Defined in: generated/model_selection/LeaveOneGroupOut.ts:21 (opens in a new tab)
Accessors
n_splits
Returns the number of splitting iterations in the cross-validator.
Signature
n_splits(): Promise<number>;Returns
Promise<number>
Defined in: generated/model_selection/LeaveOneGroupOut.ts:217 (opens in a new tab)
py
Signature
py(): PythonBridge;Returns
PythonBridge
Defined in: generated/model_selection/LeaveOneGroupOut.ts:47 (opens in a new tab)
Signature
py(pythonBridge: PythonBridge): void;Parameters
| Name | Type |
|---|---|
pythonBridge | PythonBridge |
Returns
void
Defined in: generated/model_selection/LeaveOneGroupOut.ts:51 (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/LeaveOneGroupOut.ts:104 (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? | ArrayLike | Group labels for the samples used while splitting the dataset into train/test set. This ‘groups’ parameter must always be specified to calculate the number of splits, though the other parameters can be omitted. |
opts.y? | any | Always ignored, exists for compatibility. |
Returns
Promise<number>
Defined in: generated/model_selection/LeaveOneGroupOut.ts:121 (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/LeaveOneGroupOut.ts:60 (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/LeaveOneGroupOut.ts:168 (opens in a new tab)