Validation statistics
This is a list of statistics provided by the validation service.
Type |
Value |
Synonym |
Description |
Meta information |
training_dataset_uri |
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test_dataset_uri |
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prediction_dataset_uri |
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Dataset that contains model predictions |
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prediction_feature |
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Predicted feature |
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uri |
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URI of the validation object itself |
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model_uri |
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real_runtime |
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Time needed for validation |
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id |
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created_at |
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General validation information |
num_instances |
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Number of instances in the test dataset |
num_without_class |
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Number of instances with missing class values |
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percent_without_class |
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Percent of instances with missing class values |
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num_unpredicted |
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percent_unpredicted |
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Classification information |
num_correct |
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Number of correctly classified instances |
num_incorrect |
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Number of incorrectly classified instances |
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percent_correct accuracy |
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accuracy = num_correct / num_predictions i.e. in terms of accuracy non-classified instances are NOT considered as miss-classifications. percent correct is given in percent (0-100) while accuracy is given between (0-1) |
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percent_incorrect |
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weighted_area_under_roc |
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Weighted mean (according to number of instances with the actual class value) from all area_under_roc values of all class values |
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Classification information (each value available once for each class value) |
area_under_roc |
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Area under ROC Curve |
f_measure |
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precision |
positive-predictive-value (PPV) |
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num_false_positives |
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num_false_negatives |
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num_true_positives |
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num_true_negatives |
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true_negative_rate |
specificity |
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true_positive_rate |
sensitivity, recall |
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false_negative_rate |
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false_positive_rate |
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Classification confusion matrix (each value available once for each pair of class values) |
confusion_matrix_predicted |
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Predicted class value |
confusion_matrix_actual |
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Actual class value |
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confusion_matrix_value |
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Number of instances with above actual/predicted class value |
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Regression information |
root_mean_squared_error |
RMSE, PRESS |
RMSE_CV and S_PRESS are available in the crossvalidation-report (mean and standard deviation of root_mean_squared_error) |
weighted_root_mean_squared_error |
Each squared compound prediction error is weighted according to the confidence. Sqrt( Sum(i=1 to n){(yi - fi)2 * ci} / (n * cmean) ) |
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mean_absolute_error |
MEA |
Sum(i=1 to n){(yi - fi)} / n |
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weighted_mean_absolute_error |
Each compound prediction error is weighted according to the confidence. Sum(i=1 to n){(yi - fi) * ci} / (n * cmean) |
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sum_squared_error |
residual_sum_of_squares, SS_ERR |
Sum(i=1 to n){(yi - fi)2} |
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total_sum_of_squares |
SS_TOT |
Sum(i=1 to n){(yi - ymean)2} | |
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1 - SS_ERR / SS_TOT = 1 - Sum(i=1 to n){(yi - fi)2} / Sum(i=1 to n){(yi - ymean)2} (see http://web.maths.unsw.edu.au/~adelle/Garvan/Assays/GoodnessOfFit.html, http://en.wikipedia.org/wiki/Coefficient_of_determination#Definitions) How can R² be negative? see http://www.graphpad.com/faq/viewfaq.cfm?faq=711 |
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weighted_r_square |
r² with confidence weighted predictions 1 - Sum(i=1 to n){(yi - fi)2*ci} / Sum(i=1 to n){(yi - ymean)2*ci} |
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target_variance_actual |
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the variance of the actual endpoint values 1 / (n-1) Sum(i=1 to n){(yi - ymean)2} |
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target_variance_predicted |
the variance of the predicted endpoint values 1 / (n-1) Sum(i=1 to n){(fi - fmean)2} |
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sample_correlation_coefficient |
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(defined i.e. in wikipedia: http://en.wikipedia.org/wiki/Correlation_and_dependence#Pearson.27s_product-moment_coefficient) |
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concordance_correlation_coefficient |
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defined in http://ukpmc.ac.uk/abstract/MED/2720055 (2 Sum(i=1 to n){(yi - ymean)(fi - fmean)} ) / ( Sum(i=1 to n){(yi - ymean)2} + Sum(i=1 to n){(fi - fmean)2} + n ymean fmean ) |
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Crossvalidation information |
crossvalidation_uri |
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URI of crossvalidation (if available) |
crossvalidation_fold |
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Fold of crossvalidation (if available) |