[Hamidreza Keshavarz (hamidkm9)]

Why are these changes needed?

FLAML's cheap linear slot for classification is lrl1, and a single
LogisticRegression(saga) fit is not interruptible, so a small time_budget
can be overrun by orders of magnitude (see #Issue 1612). This PR adds SEFR, a
linear-time, closed-form classifier, as an additional low-cost option.

SEFR derives one weight per feature plus a bias from class-conditional feature
means. Fitting is a closed form with no iterative optimization, and takes three
passes over the data whatever the number of classes: the feature range, the
per-class feature sums (one matrix product), and the spread of the training
scores. Beyond the scaled input it holds O(n_classes * n_features) memory.
Each binary SEFR head is n_features + 1 floats; the fitted classifier also
stores per-feature scaling parameters, one head per class for multiclass targets,
and one probability scale.

Keshavarz, Saniee Abadeh, Rawassizadeh (2020),
"SEFR: A Fast Linear-Time Classifier for Ultra-Low Power Devices", arXiv:2006.04620

Two estimators are registered, both opt-in and deliberately left out of
default_estimator_list, matching the existing treatment of histgb,
kneighbor and svc:

• "sefr" — SEFR itself. Feature scaling is part of the estimator, since
  SEFR's weight formula needs non-negative features and FLAML does not scale
  anywhere in its pipeline. Dense input is min-max scaled and clipped to [0, 1]
  at predict time; sparse input is scaled by its column max and rejected if it
  has negative entries. Margins are calibrated because FLAML's default
  multiclass metric is log_loss and raw SEFR emits margins, not probabilities.
  Margins map to calibrated logits a * margin + b (one shared scale, one bias
  per head) and to their sigmoid or softmax, so predict, decision_function
  and predict_proba always agree. The default "sigmoid" is closed form;
  "platt" fits a and b by maximum likelihood and is in the search space.
  class_weight acts as misclassification costs on this calibrated decision.
  On iris (5-fold) log loss is 0.908 uncalibrated, 0.555 with sigmoid and
  0.209 with platt.
• "sefr_boost" — AdaBoost over SEFR base learners, which gives the search
  a real but cheap-to-traverse space. It also tunes the base learner's
  calibration, starting at "platt".

No new dependencies. The algorithm is a handful of NumPy operations, and
FLAML's only required dependency is NumPy.

Budget adherence

Max wall-clock / requested time_budget over budgets of 1, 2, 5 and 10 seconds,
on the classification datasets in test/default/all/metafeatures.csv
(pandas 2.3.3, scikit-learn 1.5.2):

dataset	n_train	n_feat	n_cls	lrl1	lgbm	sefr	sefr_boost
car	1,296	6	2	1.00x	1.02x	1.00x	1.03x
dilbert	7,500	2,000	5	24.8x	1.74x	1.54x	1.56x
Amazon_employee_access	24,576	9	2	1.14x	1.67x	1.01x	1.41x
adult	36,631	14	2	1.25x	1.71x	1.01x	1.13x
connect-4	50,667	126	3	8.47x	1.54x	1.05x	1.15x
Dionis	312,141	60	355	770x	3.33x	1.68x	18.1x
Albert	318,930	78	2	10.3x	1.21x	1.12x	2.70x
Airlines	404,537	7	2	5.16x	1.10x	1.04x	1.43x
poker	768,757	10	2	5.51x	1.24x	1.04x	1.45x

sefr stays within 1.12x on 7 of 9 datasets and within 1.7x on all 9. With 355
classes, a Dionis fit takes 1.2s (sigmoid) or 4.5s (platt, whose likelihood
fit uses a row subsample once the margin matrix passes 1e7 entries).
sefr_boost fits a SEFR model per boosting round, so on Dionis it still
overruns small budgets (18.1x at 2s, 3.8x at 10s).

On accuracy, plainly

SEFR does not beat the tree learners and this PR does not claim it does.

Binary, test AUC at a 10s budget:

dataset	sefr	sefr_boost	lrl1	lgbm
car	0.941	0.947	0.941	1.000
adult	0.846	0.884	0.506	0.913
Amazon_employee_access	0.580	0.591	0.518	0.842
Albert	0.630	0.676	0.596	0.758
Airlines	0.596	0.619	0.578	0.721
poker	0.502	0.501	0.502	0.931

Multiclass, negative log loss at a 10s budget (higher is better):

dataset	sefr	sefr_boost	lrl1	lgbm
connect-4	-0.657	-0.606	-0.593	-0.384
dilbert	-1.181	-0.811	-0.238	-0.124
Dionis	-2.170	-2.204	-5.872	-2.083

SEFR clearly beats the existing cheap linear slot on 4 of 6 binary tasks, is
indistinguishable from it on poker (both at chance) and on car (0.94098 vs
0.94113), and loses to it on 2 of 3 multiclass tasks. On Dionis it comes within
0.09 of lgbm; elsewhere lgbm is clearly better.

sefr_boost beat the default pipeline on dilbert (2-10s) and connect-4 (1s). It
also scores higher on Dionis, but only while overrunning the budget (3.8x-18x),
so that is not a like-for-like win. "sefr" itself has an effectively inert search space under
roc_auc and is best understood as a constant-time baseline; "sefr_boost" is
the variant that improves with budget.

This is why both are opt-in rather than defaults.

Tests

test/automl/test_model.py adds 12 unit tests, including checks of the fitted
model against the closed form of Eqs. 3-9 recomputed independently (binary, and
each one-vs-rest head for multiclass), plus sample_weight (its effect on
calibration, zero and invalid weights), class_weight as costs, the binary Platt
intercept against LogisticRegression, the multiclass calibration and its row
subsample, sparse/dense equivalence and rejection of negative sparse input, and
sklearn's check_estimator suite, which passes in full on scikit-learn 1.0.2,
1.5.2 and 1.9.1. sefr_boost passes the SEFR learner as base_estimator on
scikit-learn < 1.2.
test/automl/test_extra_models.py adds two integration tests alongside the
other opt-in estimators.

Disclosure

I am an author of the SEFR paper. Happy to drop this if maintainers would rather
not carry it.

Related issue number

Related to #ISSUE 1612: #1612

Checks

• I've used pre-commit to lint the changes in this PR (note the same in integrated in our CI checks).
• I've included any doc changes needed for https://microsoft.github.io/FLAML/. See https://microsoft.github.io/FLAML/docs/Contribute#documentation to build and test documentation locally.
• I've added tests (if relevant) corresponding to the changes introduced in this PR.
• I've made sure all auto checks have passed.

[Hamidreza Keshavarz (hamidkm9)]

@microsoft-github-policy-service agree

@microsoft-github-policy-service agree

[Li Jiang (thinkall)]

Overall review of the complete PR: changes are required.

1. flaml/automl/contrib/sefr.py:107,234: signed sparse inputs silently use max-abs scaling, violating SEFR's required [0,1] domain and reversing predictions. Remove unsafe modes or implement/reject with guaranteed non-negative transforms.
2. flaml/automl/contrib/sefr.py:137-158: sample weights affect the SEFR head but are ignored by probability calibration; zero-weight classes can also divide by zero. Validate weights and pass effective weights into calibration.
3. flaml/automl/contrib/sefr.py:314: AdaBoostClassifier(estimator=...) is incompatible with FLAML's supported scikit-learn 1.0/1.1 floor. Support base_estimator for older APIs or raise the minimum consistently.
4. flaml/automl/contrib/sefr.py:204: fitted-state, input, feature-count, sample-weight, NaN/inf, and sparse capability validation do not satisfy basic sklearn estimator contracts. Use sklearn validation helpers and accurate tags.
5. flaml/automl/contrib/sefr.py:157 and related docs: default Platt calibration is iterative and multiclass/scaler/calibrator state exceeds the advertised single-pass and n_features + 1 model-size guarantees. Correct the implementation default or the claims.

Posted by thinkall-agent-auto-reviewer

[Hamidreza Keshavarz (hamidkm9)]

Overall review of the complete PR: changes are required.

1. flaml/automl/contrib/sefr.py:107,234: signed sparse inputs silently use max-abs scaling, violating SEFR's required [0,1] domain and reversing predictions. Remove unsafe modes or implement/reject with guaranteed non-negative transforms.
2. flaml/automl/contrib/sefr.py:137-158: sample weights affect the SEFR head but are ignored by probability calibration; zero-weight classes can also divide by zero. Validate weights and pass effective weights into calibration.
3. flaml/automl/contrib/sefr.py:314: AdaBoostClassifier(estimator=...) is incompatible with FLAML's supported scikit-learn 1.0/1.1 floor. Support base_estimator for older APIs or raise the minimum consistently.
4. flaml/automl/contrib/sefr.py:204: fitted-state, input, feature-count, sample-weight, NaN/inf, and sparse capability validation do not satisfy basic sklearn estimator contracts. Use sklearn validation helpers and accurate tags.
5. flaml/automl/contrib/sefr.py:157 and related docs: default Platt calibration is iterative and multiclass/scaler/calibrator state exceeds the advertised single-pass and n_features + 1 model-size guarantees. Correct the implementation default or the claims.

Posted by thinkall-agent-auto-reviewer

Thanks Li Jiang (@thinkall) for the review. All five points are addressed in the new commits:

1. Signed sparse input. maxabs is removed. Dense input is min-max scaled and clipped to [0, 1] at predict time. Sparse input is scaled by column max and rejected with a clear error if it has negative entries, since it can't be shifted without densifying. scaling="none" rejects negative features. scaling is no longer in the search space, since only minmax is safe for arbitrary input.
2. Sample weights. sample_weight is validated for shape, finite values and non-negativity, and classes with zero total weight raise instead of dividing by zero. The effective weights, including class_weight, now reach the calibration and the scaler's feature range. A zero weight gives the same fit as dropping the sample, and there's a test for that.
3. scikit-learn 1.0/1.1. AdaBoost gets base_estimator on scikit-learn < 1.2 and estimator otherwise. Verified on 1.0.2.
4. sklearn contracts. It now uses validate_data/_validate_data, check_is_fitted and check_classification_targets, accepts dict class_weight, and declares sparse and positive-only tags. A new test runs check_estimator, which passes on 1.0.2, 1.5.2 and 1.9.1 except check_class_weight_classifiers. That one is a documented expected failure: SEFR's class weights cancel in the per-class means and only move the Eq. 9 threshold, so it can't reach the 87% majority the check expects.
5. Calibration and claims. Binary margins map to sigmoid(a·m). Multiclass margins map to softmax(a·m + b_k), with one shared scale and a bias per class, and decision_function returns those logits. So predict, decision_function and predict_proba always agree, which wasn't guaranteed before. The default sigmoid is closed form (a from the margin spread, b_k the log class prior). platt fits a and b_k by maximum likelihood and is documented as iterative. The docstrings, docs and PR description no longer claim single-pass or n_features + 1 for the full model.
   I re-ran the budget sweep on the current code and updated the tables in the description. Binary results are unchanged. On Dionis (355 classes), sefr log loss improved from 3.07 to 2.16, within 0.08 of lgbm, and its worst overrun dropped from 18.5x to 8.6x.

[Li Jiang (thinkall)]

Overall review of the complete current PR: changes are still required.

1. flaml/automl/contrib/sefr.py:291: binary Platt calibration disables the intercept, so it cannot learn imbalanced or weighted class priors. Fit and retain a binary intercept and derive prediction consistently from the calibrated score.
2. flaml/automl/contrib/sefr.py:305: calibration subsampling can preserve a zero-weight class row while omitting every positive-weight row for that class. Preserve positive-weight coverage and revalidate effective class totals after subsampling.
3. flaml/automl/contrib/sefr.py:322: advertised class_weight does not materially affect the decision rule and the standard sklearn contract check is suppressed. Implement it correctly or remove it from the public API/search space.
4. flaml/automl/contrib/sefr.py:179: sparse support is not declared through legacy _more_tags() on supported sklearn 1.0-1.5, so meta-estimators treat SEFR as dense-only.
5. flaml/automl/contrib/sefr.py:286 and documentation: multiclass fitting materializes multiple n_samples × n_classes arrays and performs a full pass per class, contradicting the constant-pass claim and creating multi-gigabyte allocations on documented datasets. Subsample/stream before materialization and document actual complexity.

Posted by thinkall-agent-auto-reviewer

[Hamidreza Keshavarz (hamidkm9)]

Overall review of the complete current PR: changes are still required.

1. flaml/automl/contrib/sefr.py:291: binary Platt calibration disables the intercept, so it cannot learn imbalanced or weighted class priors. Fit and retain a binary intercept and derive prediction consistently from the calibrated score.
2. flaml/automl/contrib/sefr.py:305: calibration subsampling can preserve a zero-weight class row while omitting every positive-weight row for that class. Preserve positive-weight coverage and revalidate effective class totals after subsampling.
3. flaml/automl/contrib/sefr.py:322: advertised class_weight does not materially affect the decision rule and the standard sklearn contract check is suppressed. Implement it correctly or remove it from the public API/search space.
4. flaml/automl/contrib/sefr.py:179: sparse support is not declared through legacy _more_tags() on supported sklearn 1.0-1.5, so meta-estimators treat SEFR as dense-only.
5. flaml/automl/contrib/sefr.py:286 and documentation: multiclass fitting materializes multiple n_samples × n_classes arrays and performs a full pass per class, contradicting the constant-pass claim and creating multi-gigabyte allocations on documented datasets. Subsample/stream before materialization and document actual complexity.

Posted by thinkall-agent-auto-reviewer

Thanks Li Jiang (@thinkall). All five points are addressed in 7fdd46f:

1. Binary Platt intercept. Binary platt now fits a slope and an intercept by weighted maximum likelihood, and predict follows the calibrated score. A test checks it against LogisticRegression. A small fixed penalty on the slope only keeps separable data finite and doesn't depend on the scale of the weights (AdaBoost normalizes them).
2. Subsample coverage. The calibration subsample keeps a positive-weight row of every class and re-checks class totals on the subsample. A test covers the case where every row on the subsample grid, and the first row of each class, have zero weight.
3. class_weight. It is now implemented as misclassification costs on the calibrated decision. The closed-form calibration adds log(c) to each class's logit, and platt weights the likelihood by class cost. It no longer cancels in the class means. check_class_weight_classifiers now passes, so check_estimator runs with no exemptions on scikit-learn 1.0.2, 1.5.2 and 1.9.1.
4. Legacy tags. _more_tags() declares X_types: ["2darray", "sparse"] for scikit-learn < 1.6.
5. Memory and passes. Per-class sums come from one sparse one-hot matrix product. Every one-vs-rest head and its Eq. 9 bias follow in closed form, and score spreads are accumulated in row chunks. Fitting takes three passes over the data whatever the number of classes, and no n_samples × n_classes array exists except the Platt block, capped at 1e7 entries. It reproduces the previous model to about 1e-14. On Dionis (312k × 60, 355 classes), a fit went from 13.3s and 4.27 GB peak to 1.2s and 0.42 GB (sigmoid), and from 17.7s to 4.5s and 0.64 GB (platt). The docstrings and docs now describe this cost.
   I re-ran the budget sweep and updated the tables in the description. sefr now stays within 1.7x of the budget on all nine datasets (Dionis went from 8.6x to 1.7x).

[Li Jiang (thinkall)]

Overall review of the complete current PR: changes are still required.

1. flaml/automl/contrib/sefr.py:304: a model fitted on dense data learns a scaler offset, but sparse prediction input skips that offset. Identical values can produce different scores and labels solely from ndarray versus CSR representation. Apply the fitted transformation consistently or reject unsupported cross-format input.
2. flaml/automl/contrib/sefr.py:239: eps and threshold_shift are not validated. Zero/NaN values can fit successfully with non-finite coefficients or probabilities. Require finite positive eps and finite threshold_shift.

Posted by thinkall-agent-auto-reviewer

[Hamidreza Keshavarz (hamidkm9)]

Thanks Li Jiang (@thinkall). Both points are addressed in 90bc8be:

1. Dense fit, sparse predict. A model fitted on dense data with a nonzero feature minimum now applies its fitted offset to sparse input too, by densifying it, since subtracting the offset would densify it anyway. Dense and sparse input therefore always get identical scores. A test checks this for zero and nonzero minima, including values outside the training range.
2. Parameter validation. eps must be a finite positive number and threshold_shift a finite number. Anything else raises a ValueError. A test covers zero, negative, NaN, infinite, string, None and boolean values.

Is there anything else blocking this from being merged?

[Li Jiang (thinkall)]

Overall review of the complete current PR: approved.

The final two blockers are resolved. A dense-fitted SEFR model now applies its learned offset to sparse prediction input before scaling, and the new regression tests compare identical dense/CSR probes with zero and nonzero fitted minima. eps now requires a finite positive real value and threshold_shift a finite real value, with tests for invalid and valid NumPy-scalar inputs. The previously reviewed calibration, weighting, sklearn-version, tag, and multiclass-resource fixes remain unchanged. No blocking code issues found. Build-matrix CI is still running on this head.

Posted by thinkall-agent-auto-reviewer

[Hamidreza Keshavarz (hamidkm9)]

Li Jiang (@thinkall) The only failing check is build (ubuntu-latest, 3.12), which timed out in test/tune/ (unrelated to this PR). The same tune tests pass on the other 11 jobs, and all SEFR tests passed in that job before the timeout. Could you re-run the failed job?

[Copilot]

🟡 Changes recommended

Boosting-wrapper cloning fails, and calibration subsampling can distort probabilities and exceed its memory cap.

3 open issues

• Preserve existing estimator parameters during wrapper reconstruction · New
• Enforce calibration size cap after class coverage sampling · New
• Avoid biased fixed-stride sampling for calibration · New

What changed in this PR

Adds SEFR and SEFR-based AdaBoost as opt-in classification options for low-budget AutoML, without changing the default estimator list.

Changes:

• Implements SEFR with scaling, weighting, and probability calibration.
• Registers both estimators and their tuning spaces.
• Adds unit tests, integration tests, and documentation.

File	Description
website/​docs/​Use-Cases/​Task-Oriented-AutoML.md	Documents estimators and hyperparameters.
test/​automl/​test_model.py	Tests SEFR mathematics, calibration, and compatibility.
test/​automl/​test_extra_models.py	Adds AutoML integration coverage.
flaml/​automl/​task/​generic_task.py	Registers both opt-in estimators.
flaml/​automl/​contrib/​sefr.py	Implements classifiers and tuning wrappers.
flaml/​automl/​contrib/​__init__.py	Exports the new wrappers.

_{Review effort: Balanced}

[Copilot]

🟡 Changes recommended

The boosting wrapper replaces its supplied base estimator during reconstruction, breaking sklearn cloning and AutoML stacking.

4 open issues

• Preserve supplied base estimator during wrapper reconstruction · New
• Preserve existing estimator parameters during wrapper reconstruction
• Avoid biased fixed-stride sampling for calibration
• Enforce calibration size cap after class coverage sampling

_{Review effort: Balanced}

[Copilot]

🟡 Changes recommended

Valid sample weights can produce NaN predictions, boosted feature-importance access raises an exception, and calibration unnecessarily copies the feature matrix.

1 open issue

• Avoid catastrophic cancellation in complementary weighted sums · New

2 previously missed issues

In code that hasn't changed since last review

Avoid copying full feature matrix during Platt calibration

flaml/​automl/​contrib/​sefr.py:418

When Platt calibration retains all rows, rows is still an integer index array, so X[rows] copies the entire feature matrix before computing margins. For 100,000 rows and 1,000 float64 features, this adds an unnecessary 800 MB allocation per fit, repeated in each boosting round. The margin-entry cap does not bound this feature copy. Reuse X directly when all rows are retained.

Handle missing feature importances in SEFR AdaBoost wrapper

flaml/​automl/​contrib/​sefr.py:569

Reading automl.feature_importances_ after fitting sefr_boost raises ValueError. AdaBoost requires its base learners to expose feature_importances_, but SEFRClassifier only exposes coef_. The inherited FLAML property's hasattr probe propagates AdaBoost's exception. Override the wrapper property to return None when base-learner importances are unavailable, consistent with the interface in flaml/automl/model.py:239–251.

4 resolved since last review

• Preserve supplied base estimator during wrapper reconstruction
• Preserve existing estimator parameters during wrapper reconstruction
• Avoid biased fixed-stride sampling for calibration
• Enforce calibration size cap after class coverage sampling

_{Review effort: Balanced}

[Hamidreza Keshavarz (hamidkm9)]

Li Jiang (@thinkall) Both points from your last review are fixed:

1. Grouped fallback (cae9c52, tests in 32415a3). The fallback now drops groups before fitting the single SEFR model and keeps sample_weight. Your GroupKFold example is a regression test, alongside a direct fit(..., groups=...). Both raised TypeError before.
2. Subnormal weights (32415a3). The sampler normalizes the cumulative weights before scaling them by the quota, so quota / total no longer overflows. test_sefr_calibration_subsample_tiny_weights checks that the subsample of all-1e-310 weights keeps both classes and their totals, and that predictions stay finite.

On CI: the three failing build jobs fail while importing conftest.py (pyarrow requires NumPy 2.0 or newer, found 1.26.4), before any test runs. main fails the same way, so this seems to be the new pyarrow release rather than this PR.

[Copilot]

🔵 Needs a closer look

The substantial numerical implementation and cross-version integration warrant human validation, and an avoidable calibration memory allocation remains unresolved.

1 open issue

• Avoid copying full feature matrix when calibration uses all rows · New

1 resolved since last review

• Remove groups before the SEFR fallback fit

_{Review effort: Balanced}

[Li Jiang (thinkall)]

Verified the requested follow-up fixes on this head: grouped fallback no longer forwards groups to SEFRClassifier.fit, legitimate sample weights are retained, and cumulative normalization no longer overflows on the reproduced subnormal-weight case.

python -m pytest test\automl\test_model.py -k sefr -q --disable-warnings passed 18 tests (2 deselected, 2 warnings). Independent direct grouped fitting and real GroupKFold AutoML both succeeded. The 1e-310 case selected 98 rows without warnings, with nonzero class totals matching at rtol=1e-12, atol=0; the caller's kwargs also remain intact. The script-owned Low review, actual runtime gpt-6.1-sol, returned CLEAN for these incremental fixes and the inherited test-extra dependency bound.

This is not a whole-PR approval. The separate all-row calibration copy discussion remains outside this remediation delta. A small NumPy check confirms full-row advanced indexing allocates a feature-sized copy, but I did not allocate the illustrative 800 MB matrix or reproduce OOM. I am referencing the existing discussion instead of duplicating an inline thread.

Approval still requires the complete code/discussion assessment, successful actual current-head workflow runs and applicable checks, and an explicit current-head Balanced Copilot approval recommendation. Passing local tests or an incremental CLEAN result alone does not satisfy those gates. No full integration or cross-version runtime coverage is claimed.

Posted by thinkall-agent-auto-reviewer

[Copilot]

🔵 Needs a closer look

Custom calibration and cross-version boosting warrant maintainer validation, and dense scaling retains an unresolved memory-allocation issue.

1 open issue

• Avoid multiple full-sized allocations during dense min-max scaling · New

1 resolved since last review

• Avoid copying full feature matrix when calibration uses all rows

_{Review effort: Balanced}

[Copilot]

🔵 Needs a closer look

The new numerical calibration and version-dependent boosting behavior warrant final human validation.

No open issues 🎉

1 resolved since last review

• Avoid multiple full-sized allocations during dense min-max scaling

_{Review effort: Balanced}

[Li Jiang (thinkall)]

The calibration and dense-scaling memory fixes check out. I preserved the separately frozen bcaf2d review and independently reviewed the newer scaling delta on this head; both script-owned Low reviews returned CLEAN, actual runtime gpt-6.1-sol.

On the 20,000-by-100 float64 fixture, calibration's additional peak fell from 16,481,200 to 1,319,141 bytes, below the required 8,000,000-byte threshold, with unchanged calibration outputs. Dense scaling now peaks at 16,065,856 bytes, versus 48,000,760 with an offset and 32,000,664 without one previously. Outputs match at rtol=atol=1e-12; caller arrays are preserved, and public read-only/float32 prediction checks pass. The current focused suite passed 19 tests (2 deselected, 2 warnings).

I am withholding overall approval: the current Balanced Copilot review says Needs a closer look, not Approval recommended, and actual current-head CI/check completion is not fully successful. No open issues in Copilot's overview, a scoped CLEAN result, or passing local tests alone does not satisfy those mandatory gates. No broad integration or cross-version runtime execution is claimed.

Posted by thinkall-agent-auto-reviewer

[Copilot]

🔵 Needs a closer look

The numerical calibration and cross-version AdaBoost integration need human validation beyond static review.

No open issues 🎉

_{Review effort: Balanced}

[Copilot]

🔵 Needs a closer look

Custom numerical calibration, weighted subsampling, and cross-version AdaBoost behavior warrant final human validation.

No open issues 🎉

_{Review effort: Balanced}

[Copilot]

🔵 Needs a closer look

Numerical calibration, weighted subsampling, and version-dependent boosting behavior warrant final human validation across supported scikit-learn versions.

No open issues 🎉

_{Review effort: Balanced}

[Li Jiang (thinkall)]

Re-reviewed commits a87cc59453facc47419af33b272b3e905e5a18d6 -> 8f2c1e3dc2a42e46ed471f0898c8b16cae1c472d. The only delta is main's website/yarn.lock update of follow-redirects 1.16.0 -> 1.16.1. One explicitly head-bound review-code Low execution (actual runtime gpt-6.1-sol) found no new issue in this identical inherited patch. Controller Git comparisons verify all non-lockfile tree entries are unchanged. This is a scoped merge-delta review, not a fresh clean signoff for unchanged feature code.

Version/URL/caret/SRI shape checks pass. No independent local registry/tarball verification, package smoke test or website rebuild is claimed. Previous feature findings and runtime validation limits remain preserved.

At the 2026-10-26T12:13:44.943946+00:00 gate snapshot, actual current-head workflows were: Build in_progress/no conclusion; Code formatting completed/success; docs completed/success. Copilot has Balanced review without an Approval recommended verdict; 0 review thread(s) remain unresolved. Gate approval is not successful CI completion, and no new PR approval is being submitted while required gates remain unmet.

Posted by thinkall-agent-auto-reviewer