k-NN 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 Johnson-Lindenstrauss 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 sub-components 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 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 |