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:     test('should find best split when splitting the samples by real number '
          'value', () {
        final samples = Matrix.fromList([
          [10, 20, 30, 40, 0, 0, 1],
          [12, 22, 32, 42, 0, 1, 0],
          [11, 21, 31, 41, 1, 0, 0],
        ]);
  
        final outcomesRange = ZRange.closed(4, 6);
  
        final worstRange = ZRange.singleton(0);
        final worseRange = ZRange.singleton(2);
        final goodRange = ZRange.singleton(3);
        final bestRange = ZRange.singleton(1);
  
        final worstSplit = {
          DecisionTreeNodeMock(): Matrix.fromList([[1, 2, 3, 4, -1, -1, -1]]),
  
negative.code:         ]),
        };
  
        final featuresIdxs = {goodFeatureColIdx, bestFeatureColIdx};
  
        final assessor = SplitAssessorMock();
        final splitter = DecisionTreeSplitterMock();
        final categoricalValues = [1, 2, 3];
        final colIdxToUniqueValues = {ignoredFeatureColIdx: categoricalValues};
  
        when(splitter.split(observations, goodFeatureColIdx, targetColIdx))
            .thenReturn(goodSplit);
        when(splitter.split(observations, bestFeatureColIdx, targetColIdx))
            .thenReturn(bestSplit);
  
        when(assessor.getAggregatedError(goodSplit.values,
            targetColIdx)).thenReturn(0.51);
        when(assessor.getAggregatedError(bestSplit.values,
            targetColIdx)).thenReturn(0.1);
  
        final selector = GreedySplitSelector(assessor, splitter);
        final stump = selector.select(observations, targetColIdx,
            featuresIdxs, colIdxToUniqueValues);
  
        expect(stump.keys, equals(bestSplit.keys));
        expect(stump.values, equals(bestSplit.values));
  
        verifyNever(splitter.split(observations, ignoredFeatureColIdx,
            targetColIdx));
      });
    });
  }
  