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later_CL_confmat.txt
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> load(file = 'models/stacking/data_test_later_CL_classif_40')
> data_test_40 %>% dim
[1] 2692 13
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 1269 169
1 146 1803
Accuracy : 0.907
95% CI : (0.8967, 0.9166)
No Information Rate : 0.5822
P-Value [Acc > NIR] : <2e-16
Kappa : 0.8093
Mcnemar's Test P-Value : 0.2151
Sensitivity : 0.9143
Specificity : 0.8968
Pos Pred Value : 0.9251
Neg Pred Value : 0.8825
Prevalence : 0.5822
Detection Rate : 0.5323
Detection Prevalence : 0.5754
Balanced Accuracy : 0.9056
'Positive' Class : 1
> Index_temp %>% length()
[1] 42785
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 1140 136
1 122 1255
Accuracy : 0.9028
95% CI : (0.8908, 0.9138)
No Information Rate : 0.5243
P-Value [Acc > NIR] : <2e-16
Kappa : 0.8051
Mcnemar's Test P-Value : 0.4183
Sensitivity : 0.9022
Specificity : 0.9033
Pos Pred Value : 0.9114
Neg Pred Value : 0.8934
Prevalence : 0.5243
Detection Rate : 0.4730
Detection Prevalence : 0.5190
Balanced Accuracy : 0.9028
'Positive' Class : 1
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 1427 306
1 315 1339
Accuracy : 0.8167
95% CI : (0.8032, 0.8296)
No Information Rate : 0.5143
P-Value [Acc > NIR] : <2e-16
Kappa : 0.6331
Mcnemar's Test P-Value : 0.7482
Sensitivity : 0.8140
Specificity : 0.8192
Pos Pred Value : 0.8096
Neg Pred Value : 0.8234
Prevalence : 0.4857
Detection Rate : 0.3953
Detection Prevalence : 0.4883
Balanced Accuracy : 0.8166
'Positive' Class : 1
Confusion Matrix and Statistics
Reference
Prediction 0 1
0 859 147
1 119 1528
Accuracy : 0.8997
95% CI : (0.8877, 0.9109)
No Information Rate : 0.6314
P-Value [Acc > NIR] : < 2e-16
Kappa : 0.7859
Mcnemar's Test P-Value : 0.09783
Sensitivity : 0.9122
Specificity : 0.8783
Pos Pred Value : 0.9277
Neg Pred Value : 0.8539
Prevalence : 0.6314
Detection Rate : 0.5760
Detection Prevalence : 0.6208
Balanced Accuracy : 0.8953
'Positive' Class : 1