anchor.diff:      test('should find best split when splitting the samples by nominal '
           'feature', () {
         final samples = Matrix.fromList([
  -        [10, 1, 1, 1, 50, 0, 0, 1],
  -        [12, 2, 2, 2, 52, 0, 1, 0],
  -        [11, 3, 3, 3, 53, 1, 0, 0],
  +        [10, 1, 50, 1],
  +        [12, 2, 52, 2],
  +        [11, 3, 53, 3],
         ]);
   
  -      final outcomesRange = ZRange.closed(4, 6);
  -
  -      final badFeatureRange = ZRange.singleton(0);
  -      final goodFeatureRange = ZRange.singleton(4);
  -      final bestFeatureRange = ZRange.closed(1, 3);
  +      final targetColIdx = 3;
  +
  +      final badFeatureIdx = 0;
  +      final bestFeatureIdx = 1;
  +      final goodFeatureIdx = 2;
   
         final badSplit = {
           DecisionTreeNodeMock(): Matrix.fromList([
  
positive.code:         ]),
        };
  
        final featuresRanges = {
          badFeatureRange, bestFeatureRange, goodFeatureRange,
        };
  
        final assessor = SplitAssessorMock();
        final splitter = DecisionTreeSplitterMock();
        final nominalValues = [
          Vector.fromList([1, 1, 1]),
          Vector.fromList([2, 2, 2]),
          Vector.fromList([3, 3, 3]),
        ];
        final rangeToNominalValues = {bestFeatureRange: nominalValues};
  
        when(splitter.split(samples, badFeatureRange, outcomesRange))
            .thenReturn(badSplit);
        when(splitter.split(samples, goodFeatureRange, outcomesRange))
            .thenReturn(goodSplit);
        when(splitter.split(samples, bestFeatureRange, outcomesRange,
            nominalValues)).thenReturn(bestSplit);
  
        when(assessor.getAggregatedError(badSplit.values,
            outcomesRange)).thenReturn(0.999);
        when(assessor.getAggregatedError(goodSplit.values,
            outcomesRange)).thenReturn(0.4);
        when(assessor.getAggregatedError(bestSplit.values,
            outcomesRange)).thenReturn(0.1);
  
        final selector = GreedySplitSelector(assessor, splitter);
        final actualSplit = selector.select(samples, outcomesRange, featuresRanges,
            rangeToNominalValues);
  
        expect(actualSplit.keys, equals(bestSplit.keys));
        expect(actualSplit.values, equals(bestSplit.values));
  
negative.code:     test('should find best split when splitting the samples by real number '
          'value', () {
        final samples = Matrix.fromList([
          [10, 20, 30, 40, 1],
          [12, 22, 32, 42, 2],
          [11, 21, 31, 41, 3],
        ]);
  
        final targetColIdx = 4;
  
        final worstSplitIdx = 0;
        final worseSplitIdx = 2;
        final goodSplitIdx = 3;
        final bestSplitIdx = 1;
  
        final worstSplit = {
          DecisionTreeNodeMock(): Matrix.fromList([[1, 2, 3, 4, -1, -1, -1]]),
  