Class: GroupKFold
K-fold iterator variant with non-overlapping groups.
Each group will appear exactly once in the test set across all folds (the number of distinct groups has to be at least equal to the number of folds).
The folds are approximately balanced in the sense that the number of samples is approximately the same in each test fold.
Read more in the User Guide.
For visualisation of cross-validation behaviour and comparison between common scikit-learn split methods refer to Visualizing cross-validation behavior in scikit-learn
Constructors
new GroupKFold()
new GroupKFold(
opts?):GroupKFold
Parameters
| Parameter | Type | Description |
|---|---|---|
opts? | object | - |
opts.n_splits? | number | Number of folds. Must be at least 2. |
Returns GroupKFold
Defined in generated/model_selection/GroupKFold.ts:29
Properties
| Property | Type | Default value | Defined in |
|---|---|---|---|
_isDisposed | boolean | false | generated/model_selection/GroupKFold.ts:27 |
_isInitialized | boolean | false | generated/model_selection/GroupKFold.ts:26 |
_py | PythonBridge | undefined | generated/model_selection/GroupKFold.ts:25 |
id | string | undefined | generated/model_selection/GroupKFold.ts:22 |
opts | any | undefined | generated/model_selection/GroupKFold.ts:23 |
Accessors
py
Get Signature
get py():
PythonBridge
Returns PythonBridge
Set Signature
set py(
pythonBridge):void
Parameters
| Parameter | Type |
|---|---|
pythonBridge | PythonBridge |
Returns void
Defined in generated/model_selection/GroupKFold.ts:41
Methods
dispose()
dispose():
Promise<void>
Disposes of the underlying Python resources.
Once dispose() is called, the instance is no longer usable.
Returns Promise<void>
Defined in generated/model_selection/GroupKFold.ts:93
get_metadata_routing()
get_metadata_routing(
opts):Promise<any>
Get metadata routing of this object.
Please check User Guide on how the routing mechanism works.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.routing? | any | A MetadataRequest encapsulating routing information. |
Returns Promise<any>
Defined in generated/model_selection/GroupKFold.ts:112
get_n_splits()
get_n_splits(
opts):Promise<number>
Returns the number of splitting iterations in the cross-validator.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.groups? | any | Always ignored, exists for compatibility. |
opts.X? | any | Always ignored, exists for compatibility. |
opts.y? | any | Always ignored, exists for compatibility. |
Returns Promise<number>
Defined in generated/model_selection/GroupKFold.ts:146
init()
init(
py):Promise<void>
Initializes the underlying Python resources.
This instance is not usable until the Promise returned by init() resolves.
Parameters
| Parameter | Type |
|---|---|
py | PythonBridge |
Returns Promise<void>
Defined in generated/model_selection/GroupKFold.ts:54
set_split_request()
set_split_request(
opts):Promise<any>
Request metadata passed to the split method.
Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config). Please see User Guide on how the routing mechanism works.
The options for each parameter are:
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.groups? | string | boolean | Metadata routing for groups parameter in split. |
Returns Promise<any>
Defined in generated/model_selection/GroupKFold.ts:192
split()
split(
opts):Promise<ArrayLike>
Generate indices to split data into training and test set.
Parameters
| Parameter | Type | Description |
|---|---|---|
opts | object | - |
opts.groups? | ArrayLike | Group labels for the samples used while splitting the dataset into train/test set. |
opts.X? | ArrayLike[] | Training data, where n_samples is the number of samples and n_features is the number of features. |
opts.y? | ArrayLike | The target variable for supervised learning problems. |
Returns Promise<ArrayLike>
Defined in generated/model_selection/GroupKFold.ts:224