[MINOR][ML] Increase Bounded MLOR (without regularization) test error…

… tolerance

### What changes were proposed in this pull request?
Improve LogisticRegression test error tolerance

### Why are the changes needed?
When we switch BLAS version, some of the tests will fail due to too strict error tolerance in test.

### Does this PR introduce _any_ user-facing change?
No

### How was this patch tested?
N/A

Closes #30587 from WeichenXu123/fix_lor_test.

Authored-by: Weichen Xu <weichen.xu@databricks.com>
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>
(cherry picked from commit f021f6d)
Signed-off-by: Weichen Xu <weichen.xu@databricks.com>

mllib/src/test/scala/org/apache/spark/ml/classification/LogisticRegressionSuite.scala

@@ -1548,9 +1548,9 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
     val interceptsExpected1 = Vectors.dense(
       1.0000152482448372, 3.591773288423673, 5.079685953744937)
 
-    checkCoefficientsEquivalent(model1.coefficientMatrix, coefficientsExpected1)
+    checkBoundedMLORCoefficientsEquivalent(model1.coefficientMatrix, coefficientsExpected1)
     assert(model1.interceptVector ~== interceptsExpected1 relTol 0.01)
-    checkCoefficientsEquivalent(model2.coefficientMatrix, coefficientsExpected1)
+    checkBoundedMLORCoefficientsEquivalent(model2.coefficientMatrix, coefficientsExpected1)
     assert(model2.interceptVector ~== interceptsExpected1 relTol 0.01)
 
     // Bound constrained optimization with bound on both side.
@@ -1585,9 +1585,9 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
       isTransposed = true)
     val interceptsExpected3 = Vectors.dense(1.0, 2.0, 2.0)
 
-    checkCoefficientsEquivalent(model3.coefficientMatrix, coefficientsExpected3)
+    checkBoundedMLORCoefficientsEquivalent(model3.coefficientMatrix, coefficientsExpected3)
     assert(model3.interceptVector ~== interceptsExpected3 relTol 0.01)
-    checkCoefficientsEquivalent(model4.coefficientMatrix, coefficientsExpected3)
+    checkBoundedMLORCoefficientsEquivalent(model4.coefficientMatrix, coefficientsExpected3)
     assert(model4.interceptVector ~== interceptsExpected3 relTol 0.01)
 
     // Bound constrained optimization with infinite bound on both side.
@@ -1621,9 +1621,9 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
     val interceptsExpected5 = Vectors.dense(
       -2.2231282183460723, 0.3669496747012527, 1.856178543644802)
 
-    checkCoefficientsEquivalent(model5.coefficientMatrix, coefficientsExpected5)
+    checkBoundedMLORCoefficientsEquivalent(model5.coefficientMatrix, coefficientsExpected5)
     assert(model5.interceptVector ~== interceptsExpected5 relTol 0.01)
-    checkCoefficientsEquivalent(model6.coefficientMatrix, coefficientsExpected5)
+    checkBoundedMLORCoefficientsEquivalent(model6.coefficientMatrix, coefficientsExpected5)
     assert(model6.interceptVector ~== interceptsExpected5 relTol 0.01)
   }
 
@@ -1719,9 +1719,9 @@ class LogisticRegressionSuite extends MLTest with DefaultReadWriteTest {
       1.7524631428961193, 1.2292565990448736, 1.3433784431904323, 1.5846063017678864),
       isTransposed = true)
 
-    checkCoefficientsEquivalent(model1.coefficientMatrix, coefficientsExpected)
+    checkBoundedMLORCoefficientsEquivalent(model1.coefficientMatrix, coefficientsExpected)
     assert(model1.interceptVector.toArray === Array.fill(3)(0.0))
-    checkCoefficientsEquivalent(model2.coefficientMatrix, coefficientsExpected)
+    checkBoundedMLORCoefficientsEquivalent(model2.coefficientMatrix, coefficientsExpected)
     assert(model2.interceptVector.toArray === Array.fill(3)(0.0))
   }
 
@@ -2953,16 +2953,17 @@ object LogisticRegressionSuite {
   }
 
   /**
+   * Note: This method is only used in Bounded MLOR (without regularization) test
    * When no regularization is applied, the multinomial coefficients lack identifiability
    * because we do not use a pivot class. We can add any constant value to the coefficients
    * and get the same likelihood. If fitting under bound constrained optimization, we don't
    * choose the mean centered coefficients like what we do for unbound problems, since they
    * may out of the bounds. We use this function to check whether two coefficients are equivalent.
    */
-  def checkCoefficientsEquivalent(coefficients1: Matrix, coefficients2: Matrix): Unit = {
+  def checkBoundedMLORCoefficientsEquivalent(coefficients1: Matrix, coefficients2: Matrix): Unit = {
     coefficients1.colIter.zip(coefficients2.colIter).foreach { case (col1: Vector, col2: Vector) =>
       (col1.asBreeze - col2.asBreeze).toArray.toSeq.sliding(2).foreach {
-        case Seq(v1, v2) => assert(v1 ~= v2 absTol 1E-3)
+        case Seq(v1, v2) => assert(v1 ~= v2 absTol 1E-2)
       }
     }
   }