ML-008: add statistical baseline model

Closes #19

tests/ml/test_training_evaluation.py

@@ -16,18 +16,18 @@ def test_end_to_end_training_then_evaluation() -> None:
     artifact = pipeline.run("artifact_v1")
 
     evaluator = Evaluator(pipeline)
-    predictions = [
-        "artifact_v1:sensor.kitchen:{'temperature': 21.0}",
-        "artifact_v1:sensor.bedroom:{'temperature': 18.5}",
+    samples = [
+        _vector("sensor.kitchen", 21.0),
+        _vector("sensor.bedroom", 18.5),
     ]
-    report = evaluator.evaluate(artifact.artifact_id, predictions)
+    report = evaluator.evaluate(artifact.artifact_id, samples)
     assert isinstance(report, EvalReport)
-    assert report.sample_size == len(predictions)
+    assert report.sample_size == len(samples)
     assert any(metric.name == "coverage" for metric in report.metrics)
 
 
 def test_metric_helpers_are_serializable() -> None:
-    metric = Metric(name="coverage", value=0.85, threshold=0.8)
-    assert metric.name == "coverage"
+    metric = Metric(name="mae", value=0.85, threshold=1.0)
+    assert metric.name == "mae"
     assert metric.value == 0.85
-    assert metric.threshold == 0.8
+    assert metric.threshold == 1.0