# k-NN Hyperparameters
<a name="kNN_hyperparameters"></a>

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


| Parameter Name | Description | 
| --- | --- | 
| feature\$1dim |  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\$1type |  The type of inference to use on the data labels. **Required** Valid values: *classifier* for classification or *regressor* for regression.  | 
| sample\$1size |  The number of data points to be sampled from the training data set.  **Required** Valid values: positive integer  | 
| dimension\$1reduction\$1target |  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\$1reduction\$1type |  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\$1index\$1ivf\$1nlists |  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\$1index\$1pq\$1m |  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\$1metric |  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\$1PRODUCT* for inner-product distance, *COSINE* for cosine similarity. Default value: *L2*  | 
| index\$1type |  The type of index. **Optional** Valid values: *faiss.Flat*, *faiss.IVFFlat*, *faiss.IVFPQ*. Default values: *faiss.Flat*  | 
| mini\$1batch\$1size |  The number of observations per mini-batch for the data iterator.  **Optional** Valid values: positive integer Default value: 5000  |