diff --git a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_binary-features-dense.txt b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_binary-features-dense.txt
index 6b545ff8e0..db6fe9a443 100644
--- a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_binary-features-dense.txt
+++ b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_binary-features-dense.txt
@@ -12,20 +12,20 @@ DEBUG A sparse matrix is used to store the predicted labels
 INFO Successfully predicted in <duration>
 INFO Evaluation result for test data:
 
-Example-wise F1         62.02
-Example-wise Jaccard    29.62
+Example-wise F1         62.64
+Example-wise Jaccard    31.28
 Example-wise Precision  61.51
-Example-wise Recall     33.49
-Hamming Accuracy        94.62
-Hamming Loss             5.38
-Macro F1                12.52
-Macro Jaccard            6.73
-Macro Precision         92.05
-Macro Recall             8.11
-Micro F1                45.02
-Micro Jaccard           29.05
-Micro Precision         64.19
-Micro Recall            34.67
+Example-wise Recall     36.12
+Hamming Accuracy        94.68
+Hamming Loss             5.32
+Macro F1                14.35
+Macro Jaccard            7.81
+Macro Precision         88.88
+Macro Recall             9.27
+Micro F1                47.2
+Micro Jaccard           30.89
+Micro Precision         63.9
+Micro Recall            37.42
 Subset 0/1 Loss         96.98
 Subset Accuracy          3.02
 
diff --git a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_binary-features-sparse.txt b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_binary-features-sparse.txt
index 7a6d9a3ac0..ace0681ecc 100644
--- a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_binary-features-sparse.txt
+++ b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_binary-features-sparse.txt
@@ -12,20 +12,20 @@ DEBUG A sparse matrix is used to store the predicted labels
 INFO Successfully predicted in <duration>
 INFO Evaluation result for test data:
 
-Example-wise F1         61.06
-Example-wise Jaccard    29.58
-Example-wise Precision  62.75
-Example-wise Recall     33.2
-Hamming Accuracy        94.62
-Hamming Loss             5.38
-Macro F1                 9.81
-Macro Jaccard            6.25
-Macro Precision         94.94
-Macro Recall             7.58
-Micro F1                44.85
-Micro Jaccard           28.91
-Micro Precision         64.39
-Micro Recall            34.41
+Example-wise F1         62.64
+Example-wise Jaccard    31.28
+Example-wise Precision  61.51
+Example-wise Recall     36.12
+Hamming Accuracy        94.68
+Hamming Loss             5.32
+Macro F1                14.35
+Macro Jaccard            7.81
+Macro Precision         88.88
+Macro Recall             9.27
+Micro F1                47.2
+Micro Jaccard           30.89
+Micro Precision         63.9
+Micro Recall            37.42
 Subset 0/1 Loss         96.98
 Subset Accuracy          3.02
 
diff --git a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_nominal-features-dense.txt b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_nominal-features-dense.txt
index 21c06a8213..75fc6f45cd 100644
--- a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_nominal-features-dense.txt
+++ b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_nominal-features-dense.txt
@@ -12,21 +12,21 @@ DEBUG A dense matrix is used to store the predicted labels
 INFO Successfully predicted in <duration>
 INFO Evaluation result for test data:
 
-Example-wise F1         71.82
-Example-wise Jaccard    49.49
-Example-wise Precision  71.85
-Example-wise Recall     57.74
-Hamming Accuracy        77.81
-Hamming Loss            22.19
-Macro F1                60.28
-Macro Jaccard           43.89
-Macro Precision         67.51
-Macro Recall            55.34
-Micro F1                62.01
-Micro Jaccard           44.94
-Micro Precision         68.27
-Micro Recall            56.8
-Subset 0/1 Loss         75
-Subset Accuracy         25
+Example-wise F1         71.67
+Example-wise Jaccard    49.15
+Example-wise Precision  73.64
+Example-wise Recall     56.8
+Hamming Accuracy        78.4
+Hamming Loss            21.6
+Macro F1                59.79
+Macro Jaccard           43.82
+Macro Precision         69.16
+Macro Recall            53.66
+Micro F1                62.09
+Micro Jaccard           45.02
+Micro Precision         70.51
+Micro Recall            55.47
+Subset 0/1 Loss         76.02
+Subset Accuracy         23.98
 
 INFO Successfully finished after <duration>
diff --git a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_nominal-features-sparse.txt b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_nominal-features-sparse.txt
index e003954c4e..2e59815687 100644
--- a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_nominal-features-sparse.txt
+++ b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-frequency_nominal-features-sparse.txt
@@ -12,21 +12,21 @@ DEBUG A dense matrix is used to store the predicted labels
 INFO Successfully predicted in <duration>
 INFO Evaluation result for test data:
 
-Example-wise F1         70.36
-Example-wise Jaccard    48.17
-Example-wise Precision  73.89
-Example-wise Recall     55.53
-Hamming Accuracy        77.72
-Hamming Loss            22.28
-Macro F1                58.29
-Macro Jaccard           42.16
-Macro Precision         68.8
-Macro Recall            51.65
-Micro F1                60.42
-Micro Jaccard           43.29
-Micro Precision         69.69
-Micro Recall            53.33
-Subset 0/1 Loss         75.51
-Subset Accuracy         24.49
+Example-wise F1         67.86
+Example-wise Jaccard    46.6
+Example-wise Precision  76.79
+Example-wise Recall     53.32
+Hamming Accuracy        78.74
+Hamming Loss            21.26
+Macro F1                58.93
+Macro Jaccard           42.71
+Macro Precision         73.26
+Macro Recall            51.04
+Micro F1                61.18
+Micro Jaccard           44.07
+Micro Precision         73.23
+Micro Recall            52.53
+Subset 0/1 Loss         77.55
+Subset Accuracy         22.45
 
 INFO Successfully finished after <duration>
diff --git a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_binary-features-dense.txt b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_binary-features-dense.txt
index 6b545ff8e0..db6fe9a443 100644
--- a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_binary-features-dense.txt
+++ b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_binary-features-dense.txt
@@ -12,20 +12,20 @@ DEBUG A sparse matrix is used to store the predicted labels
 INFO Successfully predicted in <duration>
 INFO Evaluation result for test data:
 
-Example-wise F1         62.02
-Example-wise Jaccard    29.62
+Example-wise F1         62.64
+Example-wise Jaccard    31.28
 Example-wise Precision  61.51
-Example-wise Recall     33.49
-Hamming Accuracy        94.62
-Hamming Loss             5.38
-Macro F1                12.52
-Macro Jaccard            6.73
-Macro Precision         92.05
-Macro Recall             8.11
-Micro F1                45.02
-Micro Jaccard           29.05
-Micro Precision         64.19
-Micro Recall            34.67
+Example-wise Recall     36.12
+Hamming Accuracy        94.68
+Hamming Loss             5.32
+Macro F1                14.35
+Macro Jaccard            7.81
+Macro Precision         88.88
+Macro Recall             9.27
+Micro F1                47.2
+Micro Jaccard           30.89
+Micro Precision         63.9
+Micro Recall            37.42
 Subset 0/1 Loss         96.98
 Subset Accuracy          3.02
 
diff --git a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_binary-features-sparse.txt b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_binary-features-sparse.txt
index 7a6d9a3ac0..ace0681ecc 100644
--- a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_binary-features-sparse.txt
+++ b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_binary-features-sparse.txt
@@ -12,20 +12,20 @@ DEBUG A sparse matrix is used to store the predicted labels
 INFO Successfully predicted in <duration>
 INFO Evaluation result for test data:
 
-Example-wise F1         61.06
-Example-wise Jaccard    29.58
-Example-wise Precision  62.75
-Example-wise Recall     33.2
-Hamming Accuracy        94.62
-Hamming Loss             5.38
-Macro F1                 9.81
-Macro Jaccard            6.25
-Macro Precision         94.94
-Macro Recall             7.58
-Micro F1                44.85
-Micro Jaccard           28.91
-Micro Precision         64.39
-Micro Recall            34.41
+Example-wise F1         62.64
+Example-wise Jaccard    31.28
+Example-wise Precision  61.51
+Example-wise Recall     36.12
+Hamming Accuracy        94.68
+Hamming Loss             5.32
+Macro F1                14.35
+Macro Jaccard            7.81
+Macro Precision         88.88
+Macro Recall             9.27
+Micro F1                47.2
+Micro Jaccard           30.89
+Micro Precision         63.9
+Micro Recall            37.42
 Subset 0/1 Loss         96.98
 Subset Accuracy          3.02
 
diff --git a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_nominal-features-dense.txt b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_nominal-features-dense.txt
index 21c06a8213..75fc6f45cd 100644
--- a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_nominal-features-dense.txt
+++ b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_nominal-features-dense.txt
@@ -12,21 +12,21 @@ DEBUG A dense matrix is used to store the predicted labels
 INFO Successfully predicted in <duration>
 INFO Evaluation result for test data:
 
-Example-wise F1         71.82
-Example-wise Jaccard    49.49
-Example-wise Precision  71.85
-Example-wise Recall     57.74
-Hamming Accuracy        77.81
-Hamming Loss            22.19
-Macro F1                60.28
-Macro Jaccard           43.89
-Macro Precision         67.51
-Macro Recall            55.34
-Micro F1                62.01
-Micro Jaccard           44.94
-Micro Precision         68.27
-Micro Recall            56.8
-Subset 0/1 Loss         75
-Subset Accuracy         25
+Example-wise F1         71.67
+Example-wise Jaccard    49.15
+Example-wise Precision  73.64
+Example-wise Recall     56.8
+Hamming Accuracy        78.4
+Hamming Loss            21.6
+Macro F1                59.79
+Macro Jaccard           43.82
+Macro Precision         69.16
+Macro Recall            53.66
+Micro F1                62.09
+Micro Jaccard           45.02
+Micro Precision         70.51
+Micro Recall            55.47
+Subset 0/1 Loss         76.02
+Subset Accuracy         23.98
 
 INFO Successfully finished after <duration>
diff --git a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_nominal-features-sparse.txt b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_nominal-features-sparse.txt
index e003954c4e..2e59815687 100644
--- a/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_nominal-features-sparse.txt
+++ b/python/subprojects/testbed/tests/res/out/boomer/feature-binning-equal-width_nominal-features-sparse.txt
@@ -12,21 +12,21 @@ DEBUG A dense matrix is used to store the predicted labels
 INFO Successfully predicted in <duration>
 INFO Evaluation result for test data:
 
-Example-wise F1         70.36
-Example-wise Jaccard    48.17
-Example-wise Precision  73.89
-Example-wise Recall     55.53
-Hamming Accuracy        77.72
-Hamming Loss            22.28
-Macro F1                58.29
-Macro Jaccard           42.16
-Macro Precision         68.8
-Macro Recall            51.65
-Micro F1                60.42
-Micro Jaccard           43.29
-Micro Precision         69.69
-Micro Recall            53.33
-Subset 0/1 Loss         75.51
-Subset Accuracy         24.49
+Example-wise F1         67.86
+Example-wise Jaccard    46.6
+Example-wise Precision  76.79
+Example-wise Recall     53.32
+Hamming Accuracy        78.74
+Hamming Loss            21.26
+Macro F1                58.93
+Macro Jaccard           42.71
+Macro Precision         73.26
+Macro Recall            51.04
+Micro F1                61.18
+Micro Jaccard           44.07
+Micro Precision         73.23
+Micro Recall            52.53
+Subset 0/1 Loss         77.55
+Subset Accuracy         22.45
 
 INFO Successfully finished after <duration>