Изменения

 * Выводим результат logging.infoprint('Used params: {0}'.format(params), best_params) logging.infoprint('Evaluation:\n{0}'.format(, metrics.classification_report(expectedy_validation, predicted)))  > '''Used params: {'n_neighbors': 23}''' '''Evaluation:''' precision recall f1-score support A 0.82 1.00 0.90 40 B 0.40 0.44 0.42 43 C 0.83 0.23 0.36 22 D 0.61 0.98 0.75 47 E 0.33 0.67 0.44 42 F 0.50 0.11 0.19 70 G 0.59 0.44 0.50 68 micro avg 0.53 0.53 0.53 332 macro avg 0.58 0.55 0.51 332 weighted avg 0.56 0.53 0.49 332