v1.9.0 — split, and column importance

Both of these exist because a default lies.

michi split

A random split is right until the rows aren't independent — and then it's confidently wrong.

michi split data.csv --target churned --group customer_id
michi split data.csv --time signup_date --test-size 0.25

Strategy	When
stratified	Default for a classification target — an unstratified split on imbalanced data can starve a fold
group	Rows share an entity. Four of a customer's rows in training and one in test isn't generalisation, it's memory
time	A series. A test set drawn from before the training rows asks the model to predict the past
random	Everything else — and it now says plainly what it cannot promise

The summary states the property the split was chosen to give, then verifies it:

  No value of customer_id appears on both sides — the leak a random split
  would have caused is absent.

If a group does span both sides, it says so loudly rather than printing a guarantee that didn't hold.

eval --importance

  column        drop when shuffled         ±
  ─────────────────────────────────────────────────────────
  age                      +0.2932   0.00899
  salary                     +0.16    0.0118
  region                  +0.04764    0.0092
  signup_date            +0.000405   0.00282   within noise

  What this model uses, not what matters: a column it ignores may still drive
  the outcome, and two correlated columns split the credit between them.

Measured through predict and nothing else — so a PyTorch network and a random forest are measured identically, with no per-model introspection for michi to keep working forever.

Each column is shuffled several times and the spread reported, because an importance smaller than its own noise is not a finding. Those rows are marked rather than hidden: a column measured as unimportant is a result.

The wording is load-bearing. Reporting a bare ranking has talked people into deleting a feature that mattered, so the caveat sits under the table rather than in the docs.

Also lands as a chart in michi ui, and 分 split joins the CLI stage map.

Fixed

--importance was declared as a flag and never passed to the evaluator — it silently did nothing. Caught by running it, not by a test.

Install: pip install komichi