anchor.diff:          ]),
         };
   
  -      final featuresRanges = {
  -        badFeatureRange, bestFeatureRange, goodFeatureRange,
  -      };
  +      final featuresIdxs = {badFeatureIdx, bestFeatureIdx, goodFeatureIdx};
   
         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))
  +      final nominalValues = [1, 2, 3];
  +      final colIdxToUniqueValues = {bestFeatureIdx: nominalValues};
  +
  +      when(splitter.split(samples, badFeatureIdx, targetColIdx))
             .thenReturn(badSplit);
  -      when(splitter.split(samples, goodFeatureRange, outcomesRange))
  +      when(splitter.split(samples, goodFeatureIdx, targetColIdx))
             .thenReturn(goodSplit);
  -      when(splitter.split(samples, bestFeatureRange, outcomesRange,
  +      when(splitter.split(samples, bestFeatureIdx, targetColIdx,
             nominalValues)).thenReturn(bestSplit);
   
         when(assessor.getAggregatedError(badSplit.values,
  -          outcomesRange)).thenReturn(0.999);
  +          targetColIdx)).thenReturn(0.999);
         when(assessor.getAggregatedError(goodSplit.values,
  -          outcomesRange)).thenReturn(0.4);
  +          targetColIdx)).thenReturn(0.4);
         when(assessor.getAggregatedError(bestSplit.values,
  -          outcomesRange)).thenReturn(0.1);
  +          targetColIdx)).thenReturn(0.1);
   
         final selector = GreedySplitSelector(assessor, splitter);
  -      final actualSplit = selector.select(samples, outcomesRange, featuresRanges,
  -          rangeToNominalValues);
  +      final actualSplit = selector.select(samples, targetColIdx, featuresIdxs,
  +          colIdxToUniqueValues);
   
         expect(actualSplit.keys, equals(bestSplit.keys));
         expect(actualSplit.values, equals(bestSplit.values));
  
positive.code:         ]),
        };
  
        final featuresRanges = [
          worstRange, bestRange, worseRange, goodRange,
        ];
        final assessor = SplitAssessorMock();
        final splitter = DecisionTreeSplitterMock();
  
        when(splitter.split(samples, worstRange, outcomesRange))
            .thenReturn(worstSplit);
        when(splitter.split(samples, worseRange, outcomesRange))
            .thenReturn(worseSplit);
        when(splitter.split(samples, goodRange, outcomesRange))
            .thenReturn(goodSplit);
        when(splitter.split(samples, bestRange, outcomesRange))
            .thenReturn(bestSplit);
  
        when(assessor.getAggregatedError(worstSplit.values,
            outcomesRange)).thenReturn(0.999);
        when(assessor.getAggregatedError(worseSplit.values,
            outcomesRange)).thenReturn(0.8);
        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);
  
        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]]),
  