k-NN Hyperparameters

The following table lists the hyperparameters that you can set for the Amazon SageMaker k-nearest neighbors (k-NN) algorithm.

Parameter Name	Description
feature_dim	The number of features in the input data. Required Valid values: positive integer.
k	The number of nearest neighbors. Required Valid values: positive integer
predictor_type	The type of inference to use on the data labels. Required Valid values: classifier for classification or regressor for regression.
sample_size	The number of data points to be sampled from the training data set. Required Valid values: positive integer
dimension_reduction_target	The target dimension to reduce to. Required when you specify the dimension_reduction_type parameter. Valid values: positive integer greater than 0 and less than feature_dim.
dimension_reduction_type	The type of dimension reduction method. Optional Valid values: sign for random projection or fjlt for the fast Johnson-Lindenstrauss transform. Default value: No dimension reduction
faiss_index_ivf_nlists	The number of centroids to construct in the index when index_type is faiss.IVFFlat or faiss.IVFPQ. Optional Valid values: positive integer Default value: auto, which resolves to sqrt(sample_size).
faiss_index_pq_m	The number of vector sub-components to construct in the index when index_type is set to faiss.IVFPQ. The FaceBook AI Similarity Search (FAISS) library requires that the value of faiss_index_pq_m is a divisor of the data dimension. If faiss_index_pq_m is not a divisor of the data dimension, we increase the data dimension to smallest integer divisible by faiss_index_pq_m. If no dimension reduction is applied, the algorithm adds a padding of zeros. If dimension reduction is applied, the algorithm increase the value of the dimension_reduction_target hyper-parameter. Optional Valid values: One of the following positive integers: 1, 2, 3, 4, 8, 12, 16, 20, 24, 28, 32, 40, 48, 56, 64, 96
index_metric	The metric to measure the distance between points when finding nearest neighbors. When training with index_type set to faiss.IVFPQ, the INNER_PRODUCT distance and COSINE similarity are not supported. Optional Valid values: L2 for Euclidean-distance, INNER_PRODUCT for inner-product distance, COSINE for cosine similarity. Default value: L2
index_type	The type of index. Optional Valid values: faiss.Flat, faiss.IVFFlat, faiss.IVFPQ. Default values: faiss.Flat
mini_batch_size	The number of observations per mini-batch for the data iterator. Optional Valid values: positive integer Default value: 5000