Learn AIA new sensor site
0 / 3 submissions · Not opened on this device.
Audit a dataset and defend the evidence.
Make a comparison you can explain, record an artifact, then solve new assessment cases. Choose the kind of evidence that fits your goal.
Saved on this device with My Work export/import. Playground values are temporary until captured. Report submission, objective assessment and human review are separate. Learner pilots remain pending.
Your brief
A team proposes a flower-species classifier based on an impressive development score. Audit its evidence before supporting the claim. Use the source measurements, explain the artificial defects, and produce a comparison someone else could reconstruct.
- Capture a teaching-view run with row splitting and mean filling.
- Capture a teaching-view run with grouped splitting, training-only fitting, unit conversion and mean filling.
- Compare both with the majority baseline. Explain the changed evaluation population, not just the percentage.
Rubric: trace one calculation, identify the intervention, compare outcomes, and state a limitation. The report is learner-authored; the separate transfer check scores specific answers.
Run and capture evidence
Loading local workbench…
Captured evidence (0/4)
Scores are recalculated from your saved experiment. This is a learner record, not independent proof of how the experiment was run. Removing a run reopens the report submission but keeps assessment history.
No evidence captured yet. Use Capture run for project after a completed experiment.
Required comparison: not yet captured. This checks the experiment structure, not the quality of your reasoning.
Your experiment report
Submission needs the required comparison and nonempty report fields. Presence checks do not assess writing, correctness of the artifact, or decision quality. Any edit reopens the submission for review.
Independent transfer assessment
These new cases differ from the guided flower and neural-network examples. Work unaided: use paper or a calculator, and submit all answers before viewing feedback. Numerical answers accept a tolerance of 0.000001. Each case has four criteria; all four must pass.
First submissions measure this objective check. Up to two retries are labelled practice and cannot replace the first result. Case exposure and submissions are saved locally and retained when importing older backups. Client-side cases are inspectable; this is not a proctored credential or human review of reasoning.
First-attempt cases passed: 0 / 2. Latest attempts passed: 0 / 2. Written report and implementation review: pending.
Predict next month
0 / 3 submissions · Not opened on this device.
Targeted retry: review split design and training-only arithmetic. After exposure, a stronger independent claim needs a genuinely new case and human review.
Review your evidence
Report: draft. First-attempt objective cases: 0/2. Human review: pending. These describe different achievements; none is a general AI qualification.