Compare a rules baseline, candidate methods and independent evaluation for document classification. Synthetic samples illustrate labels, data splits, error analysis and research limits.
AI research concerns tasks, data and model methods, including comparisons, experimental design, evaluation and limitations. The document-classification example connects labeling, data splits, baselines and evaluation to research questions and validation goals.
Assess data availability, technical foundations, computing conditions and research or development capability before considering model approaches, experiments and evaluation goals.
This page introduces a service area and illustrative scenarios. Organizations can email their goals, current conditions and requirements. After receiving an enquiry, we assess feasibility, implementation conditions and whether to take on the project. Any next steps require mutual agreement.
| Stage | Protocol | Check |
|---|---|---|
| Labels & splits | Define labels; split by source document into training, validation and test sets | Keep the same document and near-duplicates in one set |
| Baseline & candidates | Freeze rules first; choose candidates and thresholds on validation data | Retain configurations; do not tune on the test set |
| Independent evaluation | Compare predictions with reference labels on the same test set | Report by class, including unknowns and error logs |
| Human review | Review mistakes, omissions and disputed labels | Describe coverage, bias and limitations |
On narrow screens, scroll sideways to view all columns.
Record TP, FP and FN by class. Precision = TP/(TP+FP); recall = TP/(TP+FN). A zero denominator is not applicable.
Method / test-set version / class / TP / FP / FN / precision / recall / error notes. Populate values from experiment records.
Consider error costs, unfamiliar topics, review workload and deployment conditions before assessing suitability.
Original illustrative scenario: parties, records and arrangements explain the service. Project scope and implementation conditions are assessed and agreed by email.
Distinguish classification, retrieval and generation, including their inputs and expected outputs.
Understand suitability and splitting when formats, labels and sources are inconsistent.
Define an evaluation set and error types before comparing rules, traditional models or other methods.
The document-classification example records documents, labels, outputs and cases requiring human review.
Agree on a baseline, data split and evaluation rules before selecting task-appropriate metrics such as precision, recall or cost.
Separate misclassification, missed labels, unclear sources and out-of-scope inputs. Approaches and feasibility require project assessment.
A short summary is enough to begin. Please leave passwords, complete sensitive datasets and confidential core technical material out of your first message.
The page defines precision, recall and an evaluation template. Synthetic labels explain the task; metric values are calculated from independent experimental records with data scope and error types.
Yes. Clarify the task and data conditions first, then consider rules, existing tools, retrieval or manual workflows. Training a model depends on the actual problem.
Prepare and copy your enquiry, open your email app, then paste into the message body, review and send it yourself.
Email goals, context and scope from your own email application.
Clarify the question and conditions, adding a summary where needed.
Assess feasibility, implementation and required resources.
If feasible, discuss scope, timing, cost and arrangements.
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