DashAI.back.models.KNeighborsClassifier

class KNeighborsClassifier(**kwargs)[source]

Scikit-learn’s K-Nearest Neighbors (KNN) classifier wrapper for DashAI.

Methods

`__init__`(**kwargs)
`fit`(x_train, y_train)	Fit the estimator.
`get_metadata_routing`()	Get metadata routing of this object.
`get_params`([deep])	Get parameters for this estimator.
`get_schema`()	Generates the component related Json Schema.
`kneighbors`([X, n_neighbors, return_distance])	Find the K-neighbors of a point.
`kneighbors_graph`([X, n_neighbors, mode])	Compute the (weighted) graph of k-Neighbors for points in X.
`load`(filename)	Load the model of the specified path.
`predict`(x_pred)	Make a prediction with the model.
`predict_proba`(X)	Return probability estimates for the test data X.
`save`(filename)	Save the model in the specified path.
`score`(X, y[, sample_weight])	Return the mean accuracy on the given test data and labels.
`set_fit_request`(*[, x_train, y_train])	Configure whether metadata should be requested to be passed to the `fit` method.
`set_params`(**params)	Set the parameters of this estimator.
`set_predict_request`(*[, x_pred])	Configure whether metadata should be requested to be passed to the `predict` method.
`set_score_request`(*[, sample_weight])	Configure whether metadata should be requested to be passed to the `score` method.
`validate_and_transform`(raw_data)	It takes the data given by the user to initialize the model and returns it with all the objects that the model needs to work.

Attributes