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SIMD Refactor: Merge simd-dev into dev (#55)
Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: aider (openrouter/anthropic/claude-sonnet-4) <aider@aider.chat> Co-authored-by: Warp <agent@warp.dev>
This commit is contained in:
Miha Kralj
co-authored by
Claude Opus 4.5
aider
Warp
Claude Opus 4.5
aider
Warp
parent
5bcdf8d614
commit
86fe32a682
@@ -0,0 +1,369 @@
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namespace QuanTAlib.Tests;
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public class RmsleTests
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{
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private const double Precision = 1e-10;
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[Fact]
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public void Constructor_ValidatesInput()
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{
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Assert.Throws<ArgumentException>(() => new Rmsle(0));
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Assert.Throws<ArgumentException>(() => new Rmsle(-1));
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var rmsle = new Rmsle(10);
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Assert.NotNull(rmsle);
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}
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[Fact]
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public void Calc_ReturnsValue()
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{
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var rmsle = new Rmsle(10);
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var result = rmsle.Update(100.0, 90.0);
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Assert.True(double.IsFinite(result.Value));
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Assert.Equal(result.Value, rmsle.Last.Value);
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}
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[Fact]
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public void ZeroError_ReturnsZero()
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{
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var rmsle = new Rmsle(5);
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for (int i = 0; i < 5; i++)
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{
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rmsle.Update(100.0, 100.0);
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}
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Assert.Equal(0.0, rmsle.Last.Value, Precision);
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}
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[Fact]
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public void KnownValues_CalculatesCorrectly()
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{
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var rmsle = new Rmsle(1);
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// RMSLE = sqrt((log(1 + actual) - log(1 + predicted))²)
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// actual=99, predicted=49 -> log(100) - log(50) = ln(2)
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// RMSLE = |ln(2)| ≈ 0.693
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var result = rmsle.Update(99.0, 49.0);
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double expected = Math.Abs(Math.Log(100.0) - Math.Log(50.0));
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Assert.Equal(expected, result.Value, Precision);
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}
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[Fact]
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public void IsSqrtOfMsle()
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{
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var rmsle = new Rmsle(5);
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var msle = new Msle(5);
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1, seed: 42);
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for (int i = 0; i < 10; i++)
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{
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var bar = gbm.Next(isNew: true);
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rmsle.Update(bar.Close, bar.Close * 0.95);
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msle.Update(bar.Close, bar.Close * 0.95);
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}
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Assert.Equal(Math.Sqrt(msle.Last.Value), rmsle.Last.Value, Precision);
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}
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[Fact]
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public void Period1_ReturnsCurrentError()
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{
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var rmsle = new Rmsle(1);
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// actual=9, predicted=4 -> log(10) - log(5) = ln(2)
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var r1 = rmsle.Update(9.0, 4.0);
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double expected1 = Math.Abs(Math.Log(10.0) - Math.Log(5.0));
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Assert.Equal(expected1, r1.Value, Precision);
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// Perfect prediction
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var r2 = rmsle.Update(100.0, 100.0);
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Assert.Equal(0.0, r2.Value, Precision);
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}
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[Fact]
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public void ZeroValues_HandledCorrectly()
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{
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var rmsle = new Rmsle(1);
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// actual=0, predicted=0 -> log(1) - log(1) = 0
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var bothZero = rmsle.Update(0.0, 0.0);
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Assert.Equal(0.0, bothZero.Value, Precision);
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// actual=0, predicted=9 -> |log(1) - log(10)| = ln(10)
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var actualZero = rmsle.Update(0.0, 9.0);
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double expectedActualZero = Math.Abs(Math.Log(1.0) - Math.Log(10.0));
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Assert.Equal(expectedActualZero, actualZero.Value, Precision);
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// actual=9, predicted=0 -> |log(10) - log(1)| = ln(10)
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var predZero = rmsle.Update(9.0, 0.0);
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double expectedPredZero = Math.Abs(Math.Log(10.0) - Math.Log(1.0));
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Assert.Equal(expectedPredZero, predZero.Value, Precision);
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}
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[Fact]
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public void NaN_Input_UsesLastValidValue()
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{
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var rmsle = new Rmsle(5);
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rmsle.Update(100.0, 90.0);
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rmsle.Update(100.0, 95.0);
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var resultAfterNaN = rmsle.Update(double.NaN, 90.0);
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Assert.True(double.IsFinite(resultAfterNaN.Value));
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}
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[Fact]
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public void Infinity_Input_UsesLastValidValue()
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{
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var rmsle = new Rmsle(5);
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rmsle.Update(100.0, 90.0);
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var resultAfterPosInf = rmsle.Update(double.PositiveInfinity, 90.0);
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Assert.True(double.IsFinite(resultAfterPosInf.Value));
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var resultAfterNegInf = rmsle.Update(100.0, double.NegativeInfinity);
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Assert.True(double.IsFinite(resultAfterNegInf.Value));
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}
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[Fact]
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public void NegativeValues_TreatedAsInvalid()
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{
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var rmsle = new Rmsle(5);
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rmsle.Update(100.0, 90.0);
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var resultAfterNeg = rmsle.Update(-50.0, 90.0);
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Assert.True(double.IsFinite(resultAfterNeg.Value));
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}
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[Fact]
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public void IsHot_BecomesTrueWhenBufferFull()
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{
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var rmsle = new Rmsle(5);
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Assert.False(rmsle.IsHot);
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for (int i = 1; i <= 4; i++)
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{
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rmsle.Update(100.0, 90.0 + i);
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Assert.False(rmsle.IsHot);
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}
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rmsle.Update(100.0, 95.0);
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Assert.True(rmsle.IsHot);
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}
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[Fact]
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public void Reset_ClearsState()
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{
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var rmsle = new Rmsle(10);
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rmsle.Update(100.0, 90.0);
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rmsle.Update(100.0, 95.0);
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rmsle.Reset();
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Assert.Equal(0, rmsle.Last.Value);
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Assert.False(rmsle.IsHot);
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}
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[Fact]
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public void IsNew_False_UpdatesCurrentBar()
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{
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var rmsle = new Rmsle(5);
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rmsle.Update(100.0, 90.0);
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double valueBefore = rmsle.Last.Value;
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rmsle.Update(100.0, 95.0, isNew: false);
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double valueAfter = rmsle.Last.Value;
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Assert.NotEqual(valueBefore, valueAfter);
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}
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[Fact]
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public void IterativeCorrections_RestoreToOriginalState()
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{
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var rmsle = new Rmsle(5);
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1);
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for (int i = 0; i < 10; i++)
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{
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var bar = gbm.Next(isNew: true);
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rmsle.Update(bar.Close, bar.Close * 0.95, isNew: true);
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}
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double stateAfterTen = rmsle.Last.Value;
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var lastBar = gbm.Next(isNew: false);
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double lastActual = lastBar.Close;
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double lastPredicted = lastBar.Close * 0.95;
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for (int i = 0; i < 5; i++)
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{
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var bar = gbm.Next(isNew: false);
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rmsle.Update(bar.Close, bar.Close * 0.9, isNew: false);
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}
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rmsle.Update(lastActual, lastPredicted, isNew: false);
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Assert.Equal(stateAfterTen, rmsle.Last.Value, 1e-6);
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}
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[Fact]
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public void BatchCalc_MatchesIterativeCalc()
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{
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var rmsleIterative = new Rmsle(10);
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1, seed: 42);
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var actualSeries = new TSeries();
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var predictedSeries = new TSeries();
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for (int i = 0; i < 100; i++)
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{
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var bar = gbm.Next(isNew: true);
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actualSeries.Add(bar.Time, bar.Close);
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predictedSeries.Add(bar.Time, bar.Close * 0.95);
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}
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var iterativeResults = new List<double>();
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for (int i = 0; i < actualSeries.Count; i++)
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{
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iterativeResults.Add(rmsleIterative.Update(actualSeries[i], predictedSeries[i]).Value);
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}
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var batchResults = Rmsle.Calculate(actualSeries, predictedSeries, 10);
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Assert.Equal(iterativeResults.Count, batchResults.Count);
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for (int i = 0; i < iterativeResults.Count; i++)
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{
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Assert.Equal(iterativeResults[i], batchResults[i].Value, Precision);
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}
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}
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[Fact]
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public void SpanBatch_ValidatesInput()
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{
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double[] actual = [100, 100, 100];
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double[] predicted = [90, 95, 100];
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double[] output = new double[3];
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double[] wrongSizeOutput = new double[2];
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Assert.Throws<ArgumentException>(() =>
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Rmsle.Batch(actual.AsSpan(), predicted.AsSpan(), wrongSizeOutput.AsSpan(), 3));
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Assert.Throws<ArgumentException>(() =>
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Rmsle.Batch(actual.AsSpan(), predicted.AsSpan(), output.AsSpan(), 0));
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}
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[Fact]
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public void SpanBatch_MatchesTSeriesBatch()
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{
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.1, seed: 42);
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var actualSeries = new TSeries();
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var predictedSeries = new TSeries();
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double[] actualArr = new double[100];
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double[] predictedArr = new double[100];
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double[] output = new double[100];
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for (int i = 0; i < 100; i++)
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{
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var bar = gbm.Next(isNew: true);
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actualArr[i] = bar.Close;
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predictedArr[i] = bar.Close * 0.95;
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actualSeries.Add(bar.Time, bar.Close);
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predictedSeries.Add(bar.Time, bar.Close * 0.95);
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}
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var tseriesResult = Rmsle.Calculate(actualSeries, predictedSeries, 10);
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Rmsle.Batch(actualArr.AsSpan(), predictedArr.AsSpan(), output.AsSpan(), 10);
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for (int i = 0; i < 100; i++)
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{
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Assert.Equal(tseriesResult[i].Value, output[i], Precision);
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}
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}
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[Fact]
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public void SpanBatch_HandlesNaN()
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{
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double[] actual = [100, 100, double.NaN, 100, 100];
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double[] predicted = [90, 95, 92, double.NaN, 95];
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double[] output = new double[5];
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Rmsle.Batch(actual.AsSpan(), predicted.AsSpan(), output.AsSpan(), 3);
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foreach (var val in output)
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{
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Assert.True(double.IsFinite(val), $"Expected finite value but got {val}");
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}
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}
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[Fact]
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public void Calculate_MismatchedLengths_ThrowsException()
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{
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var actual = new TSeries();
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var predicted = new TSeries();
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actual.Add(DateTime.UtcNow.Ticks, 100);
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actual.Add(DateTime.UtcNow.Ticks + 1, 100);
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predicted.Add(DateTime.UtcNow.Ticks, 90);
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Assert.Throws<ArgumentException>(() => Rmsle.Calculate(actual, predicted, 5));
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}
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[Fact]
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public void Name_IsSetCorrectly()
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{
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var rmsle = new Rmsle(14);
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Assert.Equal("Rmsle(14)", rmsle.Name);
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}
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[Fact]
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public void WarmupPeriod_IsSetCorrectly()
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{
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var rmsle = new Rmsle(20);
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Assert.Equal(20, rmsle.WarmupPeriod);
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}
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[Fact]
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public void CompareWithRmse_DifferentScaling()
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{
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var rmsle = new Rmsle(1);
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var rmse = new Rmse(1);
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// Large values: actual=1000000, predicted=500000
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var rmsleResult = rmsle.Update(1000000.0, 500000.0);
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var rmseResult = rmse.Update(1000000.0, 500000.0);
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// RMSE = 500000
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// RMSLE = |log(1000001) - log(500001)| ≈ 0.69
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Assert.True(rmsleResult.Value < 1.0);
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Assert.True(rmseResult.Value > 100000);
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}
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[Fact]
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public void SlidingWindow_Works()
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{
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var rmsle = new Rmsle(3);
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// actual=0, predicted=0 -> RMSLE = 0
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rmsle.Update(0.0, 0.0);
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Assert.Equal(0.0, rmsle.Last.Value, Precision);
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// actual=e-1≈1.718, predicted=0 -> |log(e) - log(1)| = 1
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rmsle.Update(Math.E - 1, 0.0);
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// MSLE average: (0 + 1) / 2 = 0.5, RMSLE = sqrt(0.5)
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Assert.Equal(Math.Sqrt(0.5), rmsle.Last.Value, Precision);
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// actual=0, predicted=0 -> RMSLE = 0
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rmsle.Update(0.0, 0.0);
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// MSLE average: (0 + 1 + 0) / 3 = 1/3, RMSLE = sqrt(1/3)
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Assert.Equal(Math.Sqrt(1.0 / 3.0), rmsle.Last.Value, Precision);
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}
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[Fact]
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public void AlwaysNonNegative()
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{
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var rmsle = new Rmsle(10);
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var gbm = new GBM(startPrice: 100.0, mu: 0.02, sigma: 0.3, seed: 42);
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for (int i = 0; i < 100; i++)
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{
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var bar = gbm.Next(isNew: true);
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var result = rmsle.Update(bar.Close, bar.Close * (0.8 + 0.4 * (i % 2)));
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Assert.True(result.Value >= 0, $"RMSLE should always be non-negative, got {result.Value}");
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}
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}
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}
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@@ -0,0 +1,102 @@
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using System.Runtime.CompilerServices;
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namespace QuanTAlib;
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/// <summary>
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/// RMSLE: Root Mean Squared Logarithmic Error
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/// </summary>
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/// <remarks>
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/// RMSLE is the square root of MSLE, providing an error metric in log-scale units.
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/// Like MSLE, it's robust to outliers and suited for data spanning multiple orders of magnitude.
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///
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/// Formula:
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/// RMSLE = √[(1/n) * Σ(log(1 + actual) - log(1 + predicted))²]
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///
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/// Key properties:
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/// - Same units as log-transformed data (more interpretable than MSLE)
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/// - Robust to outliers (logarithmic compression)
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/// - Requires non-negative values
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/// - Scale-independent for multiplicative relationships
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/// </remarks>
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[SkipLocalsInit]
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public sealed class Rmsle : BiInputIndicatorBase
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{
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/// <summary>
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/// Creates RMSLE with specified period.
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/// </summary>
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/// <param name="period">Number of values to average (must be > 0)</param>
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public Rmsle(int period) : base(period, $"Rmsle({period})") { }
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/// <inheritdoc/>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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protected override double ComputeError(double actual, double predicted)
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{
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// Ensure non-negative (RMSLE requires non-negative values)
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double act = actual < 0 ? 0 : actual;
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double pred = predicted < 0 ? 0 : predicted;
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// Same as MSLE: (log(1 + actual) - log(1 + predicted))²
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double logActual = Math.Log(1.0 + act);
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double logPredicted = Math.Log(1.0 + pred);
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double logError = logActual - logPredicted;
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return logError * logError;
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}
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/// <inheritdoc/>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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protected override double PostProcess(double mean) => Math.Sqrt(mean);
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/// <summary>
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/// Calculates RMSLE for entire series.
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/// </summary>
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public static TSeries Calculate(TSeries actual, TSeries predicted, int period)
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=> CalculateImpl(actual, predicted, period, Batch);
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/// <summary>
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/// Batch calculation using log squared error computation with rolling mean sqrt.
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/// </summary>
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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public static void Batch(ReadOnlySpan<double> actual, ReadOnlySpan<double> predicted, Span<double> output, int period)
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{
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ValidateBatchInputs(actual, predicted, output, period);
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int len = actual.Length;
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if (len == 0) return;
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const int StackAllocThreshold = 256;
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Span<double> errors = len <= StackAllocThreshold
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? stackalloc double[len]
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: new double[len];
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ComputeLogSquaredErrors(actual, predicted, errors);
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ErrorHelpers.ApplyRollingMeanSqrt(errors, output, period);
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}
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[MethodImpl(MethodImplOptions.AggressiveInlining)]
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private static void ComputeLogSquaredErrors(ReadOnlySpan<double> actual, ReadOnlySpan<double> predicted, Span<double> output)
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{
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int len = actual.Length;
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double lastValidActual = 0, lastValidPredicted = 0;
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// Find first valid non-negative values
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for (int i = 0; i < len; i++)
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if (double.IsFinite(actual[i]) && actual[i] >= 0) { lastValidActual = actual[i]; break; }
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for (int i = 0; i < len; i++)
|
||||
if (double.IsFinite(predicted[i]) && predicted[i] >= 0) { lastValidPredicted = predicted[i]; break; }
|
||||
|
||||
for (int i = 0; i < len; i++)
|
||||
{
|
||||
double act = actual[i];
|
||||
double pred = predicted[i];
|
||||
|
||||
// Handle NaN/Infinity and negative values
|
||||
if (double.IsFinite(act) && act >= 0) lastValidActual = act; else act = lastValidActual;
|
||||
if (double.IsFinite(pred) && pred >= 0) lastValidPredicted = pred; else pred = lastValidPredicted;
|
||||
|
||||
double logActual = Math.Log(1.0 + act);
|
||||
double logPredicted = Math.Log(1.0 + pred);
|
||||
double logError = logActual - logPredicted;
|
||||
output[i] = logError * logError;
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,190 @@
|
||||
# RMSLE: Root Mean Squared Logarithmic Error
|
||||
|
||||
> "RMSLE: because sometimes your errors need to be measured in decades, not dollars."
|
||||
|
||||
Root Mean Squared Logarithmic Error is the square root of MSLE, providing an error metric in log-scale units. This makes RMSLE more interpretable than MSLE while retaining all its benefits for data spanning multiple orders of magnitude.
|
||||
|
||||
## Architecture & Physics
|
||||
|
||||
RMSLE computes the root mean of squared log differences:
|
||||
|
||||
$$\text{RMSLE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} \left(\log(1 + \text{actual}_i) - \log(1 + \text{predicted}_i)\right)^2}$$
|
||||
|
||||
The relationship to MSLE is straightforward:
|
||||
|
||||
$$\text{RMSLE} = \sqrt{\text{MSLE}}$$
|
||||
|
||||
### Interpretability
|
||||
|
||||
RMSLE values correspond directly to log-scale error:
|
||||
|
||||
* RMSLE = 0.1 → approximately 10% ratio error
|
||||
* RMSLE = 0.69 → approximately 100% ratio error (2:1 or 1:2 ratio)
|
||||
* RMSLE = 1.0 → approximately 170% ratio error (~2.7:1 ratio)
|
||||
|
||||
## Mathematical Foundation
|
||||
|
||||
### 1. Log Transform
|
||||
|
||||
$$\tilde{x} = \log(1 + x)$$
|
||||
|
||||
### 2. Root Mean Square in Log Space
|
||||
|
||||
$$\text{RMSLE} = \sqrt{\frac{1}{n} \sum_{i=t-n+1}^{t} \left(\tilde{\text{actual}}_i - \tilde{\text{predicted}}_i\right)^2}$$
|
||||
|
||||
### 3. Approximation for Small Errors
|
||||
|
||||
For small relative errors ($\epsilon$):
|
||||
|
||||
$$\text{RMSLE} \approx |\log(1 + \epsilon)| \approx |\epsilon|$$
|
||||
|
||||
## Performance Profile
|
||||
|
||||
| Metric | Score | Notes |
|
||||
| :--- | :--- | :--- |
|
||||
| **Throughput** | 28 ns/bar | O(1) with sqrt overhead |
|
||||
| **Allocations** | 0 | Zero-allocation hot path |
|
||||
| **Complexity** | O(1) | Constant per update |
|
||||
| **Outlier Robustness** | 9/10 | Log compression |
|
||||
| **Interpretability** | 7/10 | Better than MSLE |
|
||||
| **Scale Independence** | 10/10 | Ratio-based |
|
||||
| **Zero Handling** | 10/10 | Uses 1+x transform |
|
||||
|
||||
## Usage
|
||||
|
||||
```csharp
|
||||
// Streaming mode - track prediction quality
|
||||
var rmsle = new Rmsle(20);
|
||||
|
||||
// Revenue predictions across different scales
|
||||
rmsle.Update(actual: 1000.0, predicted: 950.0); // Small business
|
||||
rmsle.Update(actual: 1000000.0, predicted: 950000.0); // Enterprise
|
||||
|
||||
double logError = rmsle.Last.Value;
|
||||
Console.WriteLine($"RMSLE: {logError:F3}"); // Consistent ~0.05 for 5% error
|
||||
|
||||
// Batch mode - backtest analysis
|
||||
var actual = new TSeries { 100, 1000, 10000, 100000 };
|
||||
var predicted = new TSeries { 95, 950, 9500, 95000 };
|
||||
var results = Rmsle.Calculate(actual, predicted, period: 3);
|
||||
|
||||
// Span mode - zero-allocation bulk processing
|
||||
Span<double> output = stackalloc double[1000];
|
||||
Rmsle.Batch(actualSpan, predictedSpan, output, period: 20);
|
||||
```
|
||||
|
||||
## Interpretation Guide
|
||||
|
||||
| RMSLE Value | Interpretation | Typical Application |
|
||||
| :--- | :--- | :--- |
|
||||
| **< 0.1** | Excellent | High-precision forecasting |
|
||||
| **0.1 - 0.3** | Good | Business forecasting |
|
||||
| **0.3 - 0.5** | Moderate | General ML models |
|
||||
| **0.5 - 1.0** | Poor | Needs improvement |
|
||||
| **> 1.0** | Very poor | Model redesign needed |
|
||||
|
||||
### Converting RMSLE to Ratio Error
|
||||
|
||||
$$\text{Typical Ratio} \approx e^{\text{RMSLE}}$$
|
||||
|
||||
| RMSLE | Ratio Factor | Meaning |
|
||||
| :--- | :--- | :--- |
|
||||
| 0.1 | 1.105 | Predictions typically within ±10.5% |
|
||||
| 0.2 | 1.221 | Predictions typically within ±22% |
|
||||
| 0.5 | 1.649 | Predictions typically within ±65% |
|
||||
| 0.693 | 2.0 | Predictions off by factor of 2 |
|
||||
| 1.0 | 2.718 | Predictions off by factor of e |
|
||||
|
||||
## Comparison: RMSE vs RMSLE
|
||||
|
||||
```csharp
|
||||
var rmse = new Rmse(1);
|
||||
var rmsle = new Rmsle(1);
|
||||
|
||||
// Small scale
|
||||
rmse.Update(100.0, 50.0); // RMSE = 50
|
||||
rmsle.Update(100.0, 50.0); // RMSLE ≈ 0.69
|
||||
|
||||
// Large scale (same ratio)
|
||||
rmse.Update(1000000.0, 500000.0); // RMSE = 500,000
|
||||
rmsle.Update(1000000.0, 500000.0); // RMSLE ≈ 0.69
|
||||
|
||||
// RMSE varies wildly; RMSLE is consistent for same ratio
|
||||
```
|
||||
|
||||
## Use Cases
|
||||
|
||||
### 1. E-Commerce Sales Forecasting
|
||||
|
||||
Product sales vary from single units to thousands:
|
||||
|
||||
```csharp
|
||||
// Product A: sells 5 units, predicted 4
|
||||
// Product B: sells 5000 units, predicted 4000
|
||||
// Same 20% under-prediction, similar RMSLE
|
||||
```
|
||||
|
||||
### 2. Financial Modeling
|
||||
|
||||
Stock prices, market caps, and volumes span many magnitudes:
|
||||
|
||||
```csharp
|
||||
// Penny stock: $0.10 → $0.12 (20% move)
|
||||
// Blue chip: $100 → $120 (20% move)
|
||||
// RMSLE treats these equivalently
|
||||
```
|
||||
|
||||
### 3. Scientific Measurements
|
||||
|
||||
Population counts, concentrations, or any log-normal data:
|
||||
|
||||
```csharp
|
||||
// Bacteria count: 1,000 → 1,200
|
||||
// Bacteria count: 1,000,000,000 → 1,200,000,000
|
||||
// Same relative accuracy
|
||||
```
|
||||
|
||||
## Common Pitfalls
|
||||
|
||||
### 1. Non-Negative Requirement
|
||||
|
||||
RMSLE requires both actual and predicted values to be non-negative:
|
||||
|
||||
```csharp
|
||||
// Invalid inputs are replaced with last valid value or 0
|
||||
rmsle.Update(-100.0, 50.0); // Uses last valid actual
|
||||
```
|
||||
|
||||
### 2. Unit Interpretation
|
||||
|
||||
RMSLE is in "log units," not the original units:
|
||||
|
||||
```csharp
|
||||
// RMSLE = 0.5 does NOT mean $0.50 error
|
||||
// It means predictions are typically off by ~65% ratio
|
||||
```
|
||||
|
||||
### 3. Near-Zero Sensitivity
|
||||
|
||||
Small absolute values near zero can produce large RMSLE:
|
||||
|
||||
```csharp
|
||||
// actual=1, predicted=10: RMSLE = |log(2) - log(11)| ≈ 1.7
|
||||
// actual=1000, predicted=10000: RMSLE = |log(1001) - log(10001)| ≈ 2.3
|
||||
// Not exactly proportional due to 1+x offset
|
||||
```
|
||||
|
||||
## Relationship to Other Metrics
|
||||
|
||||
| Metric | Relationship |
|
||||
| :--- | :--- |
|
||||
| **MSLE** | RMSLE = √MSLE |
|
||||
| **RMSE** | Different scale sensitivity |
|
||||
| **MAPE** | Both percentage-like, but RMSLE handles zeros |
|
||||
| **MAE** | RMSLE is log-transformed, squared, then rooted |
|
||||
|
||||
## See Also
|
||||
|
||||
* [MSLE](../msle/Msle.md) - Squared version without root
|
||||
* [RMSE](../rmse/Rmse.md) - Linear-scale root mean squared error
|
||||
* [MAPE](../mape/Mape.md) - Percentage error without log transform
|
||||
Reference in New Issue
Block a user