Analysis of effects of different similarity measures—Pearson Correlation results for 10-fold cross-validation using Leave-one-Out feature strategy (i.e. the model is trained on all features except the one mentioned in each row) and results for each measure individually (i.e. the model is trained only for the mentioned feature).
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https://figshare.com/articles/dataset/_Analysis_of_effects_of_different_similarity_measures_8212_Pearson_Correlation_results_for_10_fold_cross_validation_using_Leave_one_Out_feature_strategy_i_e_the_model_is_trained_on_all_features_except_the_one_mentioned_in_each_row_and_results_for_each_mea/1435168
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Analysis of effects of different similarity measures—Pearson Correlation results for 10-fold cross-validation using Leave-one-Out feature strategy (i.e. the model is trained on all features except the one mentioned in each row) and results for each measure individually (i.e. the model is trained only for the mentioned feature).
创建时间：
2015-12-03
