Created
November 26, 2017 12:41
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k-NN metrics
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| hits = np.asarray([cnf_matrix[i][i] for i in range(cnf_matrix.shape[0])]) | |
| expected = np.sum(cnf_matrix, axis=0) | |
| num_hits = np.sum(hits) | |
| total = test_data.shape[0] | |
| precision = 100 * (num_hits / total) | |
| recall_per_class = hits / expected | |
| recall = 100 * (np.sum(recall_per_class) / recall_per_class.shape[0]) |
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