This function finds the K number of training examples closest (nearest neighbors) to the input data and then classifying the input data based on the majority class of its nearest neighbors.
In other words, it assigns a label to a new data point based on how similar it is to the existing data points, where similarity is defined by distance metric such as Euclidean or Manhattan.
This function can be used for both supervised and unsupervised learning.
Controls the randomness of the estimator (defaults to 0).
n_neighbors
int
Number of neighbors to use by default for kneighbors queries (defaults to 5).
distance
String
Metric to use for distance computation. Default minkowski, which results in the standard Euclidean distance when p = 2.
p
int
Power parameter for the Minkowski metric. When p = 1, this is equivalent to using manhattan_distance, and euclidean_distance for p = 2 (defaults to 2).
model_name*
String
Name of previously trained model to be used for prediction
test_data
String
Input data for prediction. Defaults to x_train_url in StateManagment
project_id*
int
ID of the current project
block_id*
int
ID of the current block
parent_id*
int
ID of the previous block
function_code*
String
Function code for the current block
project_id*
int
ID of the current project
parent_id*
int
ID of the previous block
block_id*
int
ID of the current block
function_code*
String
Function code for the current block
model_name*
String
Name of the pre-trained model to be used for evaluation