Knn Hyperparameters
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
Valid values: positive integer greater than 0 and
less
than 
dimension_reduction_type 
The type of dimension reduction method. Optional Valid values: sign for random projection or fjlt for the fast JohnsonLindenstrauss transform. Default value: No dimension reduction 
faiss_index_ivf_nlists 
The number of centroids to construct in the index when
Optional Valid values: positive integer Default value:
auto,
which resolves to

faiss_index_pq_m 
The number of vector subcomponents to construct in the index when
The
FaceBook
AI Similarity Search (FAISS) library requires that the value of
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 Optional Valid values: L2 for Euclideandistance, INNER_PRODUCT for innerproduct 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 minibatch for the data iterator. Optional Valid values: positive integer Default value: 5000 