Tidy Characterizations of Model Performance

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Documentation for package ‘yardstick’ version 0.0.1

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accuracy Classification Metrics on Predited Classes
accuracy.data.frame Classification Metrics on Predited Classes
accuracy.matrix Classification Metrics on Predited Classes
accuracy.table Classification Metrics on Predited Classes
ccc Calculate Metrics for Numeric Outcomes
ccc.data.frame Calculate Metrics for Numeric Outcomes
conf_mat Confusion Matrix for Categorical Data
conf_mat.data.frame Confusion Matrix for Categorical Data
conf_mat.default Confusion Matrix for Categorical Data
conf_mat.table Confusion Matrix for Categorical Data
f_meas Calculate recall, precision and F values
f_meas.default Calculate recall, precision and F values
f_meas.table Calculate recall, precision and F values
hpc_cv Class Probability Predictions
j_index Other Metrics for 2x2 Tables
j_index.data.frame Other Metrics for 2x2 Tables
j_index.table Other Metrics for 2x2 Tables
mae Calculate Metrics for Numeric Outcomes
mae.data.frame Calculate Metrics for Numeric Outcomes
mcc Other Metrics for 2x2 Tables
mcc.data.frame Other Metrics for 2x2 Tables
mcc.table Other Metrics for 2x2 Tables
metrics General Function to Estimate Performance
metrics.data.frame General Function to Estimate Performance
mnLogLoss Metrics Based on Class Probabilities
mnLogLoss.data.frame Metrics Based on Class Probabilities
npv Calculate sensitivity, specificity and predictive values
npv.default Calculate sensitivity, specificity and predictive values
npv.matrix Calculate sensitivity, specificity and predictive values
npv.table Calculate sensitivity, specificity and predictive values
pathology Liver Pathology Data
ppv Calculate sensitivity, specificity and predictive values
ppv.default Calculate sensitivity, specificity and predictive values
ppv.matrix Calculate sensitivity, specificity and predictive values
ppv.table Calculate sensitivity, specificity and predictive values
precision Calculate recall, precision and F values
precision.data.frame Calculate recall, precision and F values
precision.default Calculate recall, precision and F values
precision.matrix Calculate recall, precision and F values
precision.table Calculate recall, precision and F values
pr_auc Metrics Based on Class Probabilities
pr_auc.data.frame Metrics Based on Class Probabilities
pr_auc.default Metrics Based on Class Probabilities
recall Calculate recall, precision and F values
recall.data.frame Calculate recall, precision and F values
recall.default Calculate recall, precision and F values
recall.table Calculate recall, precision and F values
rmse Calculate Metrics for Numeric Outcomes
rmse.data.frame Calculate Metrics for Numeric Outcomes
roc_auc Metrics Based on Class Probabilities
roc_auc.data.frame Metrics Based on Class Probabilities
roc_auc.default Metrics Based on Class Probabilities
rsq Calculate Metrics for Numeric Outcomes
rsq.data.frame Calculate Metrics for Numeric Outcomes
rsq_trad Calculate Metrics for Numeric Outcomes
rsq_trad.data.frame Calculate Metrics for Numeric Outcomes
sens Calculate sensitivity, specificity and predictive values
sens.data.frame Calculate sensitivity, specificity and predictive values
sens.default Calculate sensitivity, specificity and predictive values
sens.matrix Calculate sensitivity, specificity and predictive values
sens.table Calculate sensitivity, specificity and predictive values
solubility_test Solubility Predictions from MARS Model
spec Calculate sensitivity, specificity and predictive values
spec.data.frame Calculate sensitivity, specificity and predictive values
spec.default Calculate sensitivity, specificity and predictive values
spec.matrix Calculate sensitivity, specificity and predictive values
spec.table Calculate sensitivity, specificity and predictive values
summary.conf_mat Summary Statistics for Confusion Matrices
tidy.conf_mat Confusion Matrix for Categorical Data
two_class_example Two Class Predictions