NetCore3.1 generates different test result issue

test/BaselineOutput/Common/Command/netcoreapp31/CommandTrainMlrWithStats-out.txt

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+maml.exe Train tr=MultiClassLogisticRegression{maxiter=100 t=- stat=+} loader=TextLoader{col=Label:TX:4 col=Features:R4:0-3 sep=,} data=%Data% out=%Output% seed=1 xf=Term{col=Label}
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Beginning optimization
+num vars: 15
+improvement criterion: Mean Improvement
+L1 regularization selected 10 of 15 weights.
+Model trained with 150 training examples.
+Residual Deviance: 	132.4371
+Null Deviance:    	329.58368
+AIC:              	152.4371
+Not training a calibrator because it is not needed.
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+

test/BaselineOutput/Common/Command/netcoreapp31/CommandTrainMlrWithStats-summary.txt

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+LbfgsMaximumEntropyMulticlassTrainer bias and non-zero weights
+	Iris-setosa+(Bias)	2.2171915
+	Iris-versicolor+(Bias)	0.76931
+	Iris-virginica+(Bias)	-2.9864972
+	Iris-setosa+f3	-3.179184
+	Iris-setosa+f2	-2.8718326
+	Iris-setosa+f1	0.5830593
+	Iris-versicolor+f1	-0.68959576
+	Iris-virginica+f3	3.145027
+	Iris-virginica+f2	1.882819
+	Iris-virginica+f0	0.0037482954
+
+*** MODEL STATISTICS SUMMARY ***   
+Count of training examples:	150
+Residual Deviance:         	132.4371
+Null Deviance:             	329.58368
+AIC:                       	152.4371

test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer-out.txt

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+maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 8 rows with missing feature/label values
+Training calibrator.
+Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off.
+Warning: Skipped 8 rows with missing feature/label values
+Training calibrator.
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3785 (134.0/(134.0+220.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      134 |        0 | 1.0000
+ negative ||       12 |      208 | 0.9455
+          ||======================
+Precision ||   0.9178 |   1.0000 |
+OVERALL 0/1 ACCURACY: 0.966102
+LOG LOSS/instance:  0.121887
+Test-set entropy (prior Log-Loss/instance): 0.956998
+LOG-LOSS REDUCTION (RIG): 0.872636
+AUC:                0.994437
+Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable.
+TEST POSITIVE RATIO:	0.3191 (105.0/(105.0+224.0))
+Confusion table
+          ||======================
+PREDICTED || positive | negative | Recall
+TRUTH     ||======================
+ positive ||      101 |        4 | 0.9619
+ negative ||        5 |      219 | 0.9777
+          ||======================
+Precision ||   0.9528 |   0.9821 |
+OVERALL 0/1 ACCURACY: 0.972644
+LOG LOSS/instance:  0.160647
+Test-set entropy (prior Log-Loss/instance): 0.903454
+LOG-LOSS REDUCTION (RIG): 0.822185
+AUC:                0.984694
+
+OVERALL RESULTS
+---------------------------------------
+AUC:                0.989565 (0.0049)
+Accuracy:           0.969373 (0.0033)
+Positive precision: 0.935319 (0.0175)
+Positive recall:    0.980952 (0.0190)
+Negative precision: 0.991031 (0.0090)
+Negative recall:    0.961567 (0.0161)
+Log-loss:           0.141267 (0.0194)
+Log-loss reduction: 0.847411 (0.0252)
+F1 Score:           0.957244 (0.0001)
+AUPRC:              0.986457 (0.0034)
+
+---------------------------------------
+Physical memory usage(MB): %Number%
+Virtual memory usage(MB): %Number%
+%DateTime%	 Time elapsed(s): %Number%
+

test/BaselineOutput/Common/LdSvm/netcoreapp31/LDSVM-def-CV-breast-cancer-rp.txt

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+LdSvm
+AUC	Accuracy	Positive precision	Positive recall	Negative precision	Negative recall	Log-loss	Log-loss reduction	F1 Score	AUPRC	/iter	Learner Name	Train Dataset	Test Dataset	Results File	Run Time	Physical Memory	Virtual Memory	Command Line	Settings	
+0.989565	0.969373	0.935319	0.980952	0.991031	0.961567	0.141267	0.847411	0.957244	0.986457	1000	LdSvm	%Data%		%Output%	99	0	0	maml.exe CV tr=LdSvm{iter=1000} threads=- dout=%Output% data=%Data% seed=1	/iter:1000	
+