.. _sphx_glr_auto_examples_nearest_neighbor:

Nearest Neighbor Models
=======================

Examples for data valuation using efficient explanations of nearest-neighbor models.



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    <div class="sphx-glr-thumbcontainer" tooltip="This notebook shows how explainers of nearest-neighbor (NN) models can be used for Data Valuation, the task of evaluating the usefulness of individual training data points in classification problems. When explaining NN models, a game is defined by first choosing an explanation point x_\text{explain} and class y_\text{explain}; the training data points \mathcal{D} := \mathcal{X} \times \mathcal{Y} are the game&#x27;s players, and the definition of the utility \nu(S) of a coalition S \subseteq \mathcal{D} is based on the probability of the model predicting class y_\text{explain} on x_\text{explain} if it&#x27;s training data were limited to S.">

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  :doc:`/auto_examples/nearest_neighbor/plot_nn_data_valuation`

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      <div class="sphx-glr-thumbnail-title">Data Valuation with Nearest Neighbor Explainers</div>
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.. toctree::
   :hidden:

   /auto_examples/nearest_neighbor/plot_nn_data_valuation