# 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\_dim | The number of features in the input data.<br />**Required**<br />Valid values: positive integer. | 
| k | The number of nearest neighbors.<br />**Required**<br />Valid values: positive integer | 
| predictor\_type | The type of inference to use on the data labels.<br />**Required**<br />Valid values: *classifier* for classification or *regressor* for regression. | 
| sample\_size | The number of data points to be sampled from the training data set. <br />**Required**<br />Valid values: positive integer | 
| dimension\_reduction\_target | The target dimension to reduce to.<br />**Required** when you specify the `dimension_reduction_type` parameter.<br />Valid values: positive integer greater than 0 and less than `feature_dim`. | 
| dimension\_reduction\_type | The type of dimension reduction method. <br />**Optional**<br />Valid values: *sign* for random projection or *fjlt* for the fast Johnson-Lindenstrauss transform.<br />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*.<br />**Optional**<br />Valid values: positive integer<br />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*. <br />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.<br />**Optional**<br />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.<br />**Optional**<br />Valid values: *L2* for Euclidean-distance, *INNER\_PRODUCT* for inner-product distance, *COSINE* for cosine similarity.<br />Default value: *L2* | 
| index\_type | The type of index.<br />**Optional**<br />Valid values: *faiss.Flat*, *faiss.IVFFlat*, *faiss.IVFPQ*.<br />Default values: *faiss.Flat* | 
| mini\_batch\_size | The number of observations per mini-batch for the data iterator. <br />**Optional**<br />Valid values: positive integer<br />Default value: 5000 |