Matches in Nanopublications for { ?s <http://www.w3.org/2000/01/rdf-schema#label> ?o ?g. }
- STATO_0000415 label "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." assertion.
- STATO_0000415 label "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." assertion.
- STATO_0000415 label "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." assertion.
- STATO_0000415 label "accuracy - in the context of binary classification, accuracy is defined as the proportion of true results (both true positives and true negatives) to the total number of cases examined (the sum of true positive, true negative, false positive and false negative). It can be understood as a measure of the proximity of measurement results to the true value." assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- STATO_0000416 label "precision - precision or positive predictive value is defined as the proportion of the true positives against all the positive results (both true positives and false positives)" assertion.
- dataset-label label "the name of the dataset" assertion.
- algorithm label "the used algorithm (e.g. Random Forest)" assertion.
- assertion label "Publishing the evaluation result of a Machine Learning experiment" assertion.
- dataset label "the URL of the used dataset" assertion.
- evaluation label "an evaluation" assertion.
- evaluation-measure label "the evaluation measure (e.g. Accuracy)" assertion.
- evaluation-value label "value of the evaluation result (e.g. 0.81 if the accuracy is 81%)" assertion.
- run label "this run" assertion.
- task label "the task performed (e.g. Spam Detection)" assertion.
- run label "this run" assertion.
- algorithm label "the used algorithm (e.g. Random Forest)" assertion.
- assertion label "Publishing the evaluation result of a Machine Learning experiment" assertion.
- dataset label "the URL of the used dataset" assertion.
- dataset-label label "the name of the dataset" assertion.
- evaluation label "an evaluation" assertion.
- evaluation-measure label "the evaluation measure (e.g. Accuracy)" assertion.
- evaluation-value label "value of the evaluation result (e.g. 0.81 if the accuracy is 81%)" assertion.
- run-label label "name or label of the experiment run" assertion.
- task label "the task performed (e.g. Spam Detection)" assertion.
- Q6975395 label "F1 score - measure of a test's accuracy" assertion.
- Q6975395 label "F1 score - measure of a test's accuracy" assertion.
- Q6975395 label "F1 score - measure of a test's accuracy" assertion.
- Q6975395 label "F1 score - measure of a test's accuracy" assertion.
- Q6975395 label "F1 score - measure of a test's accuracy" assertion.
- Q6975395 label "F1 score - measure of a test's accuracy" assertion.
- Q6975395 label "F1 score - measure of a test's accuracy" assertion.
- Q6975395 label "F1 score - measure of a test's accuracy" assertion.
- Q6975395 label "F1 score - measure of a test's accuracy" assertion.
- Q6975395 label "F1 score - measure of a test's accuracy" assertion.
- Q6975395 label "F1 score - measure of a test's accuracy" assertion.
- FIP.15.T8 label "FIP.15.T8 | PARC FIP1 T8" assertion.
- algorithm label "the used algorithm (e.g. Random Forest)" assertion.
- assertion label "Publishing the evaluation result of a Machine Learning experiment" assertion.
- dataset label "the URL of the used dataset" assertion.
- dataset-label label "the name of the dataset" assertion.
- evaluation label "an evaluation" assertion.
- evaluation-measure label "the evaluation measure (e.g. Accuracy)" assertion.
- evaluation-value label "value of the evaluation result (e.g. 0.81 if the accuracy is 81%)" assertion.
- run label "this run" assertion.
- run-label label "name or label of the experiment run" assertion.
- task label "the task performed (e.g. Spam Detection)" assertion.
- algorithm label "the used algorithm (e.g. Random Forest)" assertion.
- assertion label "Publishing the evaluation result of a Machine Learning experiment" assertion.
- dataset label "the URL of the used dataset" assertion.
- dataset-label label "the name of the dataset" assertion.
- evaluation label "an evaluation" assertion.
- evaluation-measure label "the evaluation measure (e.g. Accuracy)" assertion.
- evaluation-value label "value of the evaluation result (e.g. 0.81 if the accuracy is 81%)" assertion.
- run label "this run" assertion.
- run-description label "longer description of the experiment run" assertion.
- run-label label "name or label of the experiment run" assertion.
- task label "the task performed (e.g. Spam Detection)" assertion.
- algorithm label "the used algorithm (e.g. Random Forest)" assertion.
- assertion label "Publishing the evaluation result of a Machine Learning experiment" assertion.
- dataset label "the URL of the used dataset" assertion.
- dataset-label label "the name of the dataset" assertion.
- evaluation label "an evaluation" assertion.
- evaluation-measure label "the evaluation measure (e.g. Accuracy)" assertion.
- evaluation-value label "value of the evaluation result (e.g. 0.81 if the accuracy is 81%)" assertion.
- run label "this run" assertion.
- run-description label "longer description of the experiment run" assertion.
- run-label label "name or label of the experiment run" assertion.
- task label "the task performed (e.g. Spam Detection)" assertion.
- algorithm label "the used algorithm (e.g. Random Forest)" assertion.
- assertion label "Publishing the evaluation result of a Machine Learning experiment" assertion.
- dataset label "the URL of the used dataset" assertion.
- dataset-label label "the name of the dataset" assertion.
- evaluation label "an evaluation" assertion.
- evaluation-measure label "the evaluation measure (e.g. Accuracy)" assertion.
- evaluation-value label "value of the evaluation result (e.g. 0.81 if the accuracy is 81%)" assertion.
- run label "this run" assertion.
- run-description label "longer description of the experiment run" assertion.
- run-label label "name or label of the experiment run" assertion.
- task label "the task performed (e.g. Spam Detection)" assertion.
- 77ba-9x74 label "The New York Times Annotated Corpus" assertion.
- 77ba-9x74 label "The New York Times Annotated Corpus" assertion.
- 77ba-9x74 label "The New York Times Annotated Corpus" assertion.
- 77ba-9x74 label "The New York Times Annotated Corpus" assertion.
- run label "Detection of texts about animals" assertion.
- algorithm label "the used algorithm (e.g. Random Forest)" assertion.
- assertion label "Publishing the evaluation result of a Machine Learning experiment" assertion.
- dataset label "the URL of the used dataset" assertion.
- dataset-label label "the name of the dataset" assertion.