- Info
Validation statistics
This is a list of statistics provided by the validation service.
Type
|
Value
|
Synonym
|
Description
|
Meta
information
|
training_dataset_uri
|
|
|
test_dataset_uri
|
|
|
prediction_dataset_uri
|
|
Dataset that contains model predictions
|
prediction_feature
|
|
Predicted
feature
|
uri
|
|
URI of the validation object itself
|
model_uri
|
|
|
real_runtime
|
|
Time
needed for validation
|
id
|
|
|
created_at
|
|
|
General
validation information
|
num_instances
|
|
Number of instances in the test dataset
|
num_without_class
|
|
Number of instances with missing class values
|
percent_without_class
|
|
Percent of instances with missing class values
|
num_unpredicted
|
|
|
percent_unpredicted
|
|
|
Classification
information
|
num_correct
|
|
Number of correctly classified instances
|
num_incorrect
|
|
Number of incorrectly classified instances
|
percent_correct
accuracy
|
|
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)
|
percent_incorrect
|
|
|
weighted_area_under_roc
|
|
Weighted mean (according to number of instances with
the actual class value) from all area_under_roc values of all class values
|
Classification information
(each value available once for each class value)
|
area_under_roc
|
|
Area under ROC Curve
|
f_measure
|
|
|
precision
|
positive-predictive-value (PPV)
|
|
num_false_positives
|
|
|
num_false_negatives
|
|
|
num_true_positives
|
|
|
num_true_negatives
|
|
|
true_negative_rate
|
specificity
|
|
true_positive_rate
|
sensitivity, recall
|
|
false_negative_rate
|
|
|
false_positive_rate
|
|
|
Classification
confusion matrix
(each value available once for each pair of class
values)
|
confusion_matrix_predicted
|
|
Predicted
class value
|
confusion_matrix_actual
|
|
Actual
class value
|
confusion_matrix_value
|
|
Number of instances with above actual/predicted
class value
|
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) )
|
mean_absolute_error
|
MEA
|
Sum(i=1 to n){(yi - fi)} / n
|
weighted_mean_absolute_error
|
|
Each compound prediction error is weighted according to the confidence. Sum(i=1 to n){(yi - fi) * ci} / (n * cmean)
|
sum_squared_error
|
residual_sum_of_squares, SS_ERR
|
Sum(i=1 to n){(yi - fi)2}
|
total_sum_of_squares
|
SS_TOT
|
Sum(i=1 to n){(yi
- ymean)2} |
r_square
|
|
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
|
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} |
target_variance_actual
|
|
the variance of the actual endpoint values
1 / (n-1) Sum(i=1 to n){(yi - ymean)2}
|
target_variance_predicted |
|
the variance of the predicted endpoint values
1 / (n-1) Sum(i=1 to n){(fi - fmean)2}
|
sample_correlation_coefficient
|
|
(defined i.e. in wikipedia: http://en.wikipedia.org/wiki/Correlation_and_dependence#Pearson.27s_product-moment_coefficient)
|
concordance_correlation_coefficient
|
|
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 )
|
Crossvalidation
information
|
crossvalidation_uri
|
|
URI of crossvalidation (if available)
|
crossvalidation_fold
|
|
Fold of crossvalidation (if available)
|