Analyses

Upload your data, tell Diafunc what you want to understand, and get back a report that reveals the relationships in your data — written as formulas you can defend in a meeting, illustrated with charts, and willing to answer follow-up questions.

An analysis is a guided run powered by the Ufinq symbolic regression technology. The resulting report is a living notebook inside a Diafunc project — editable, rerunnable, and shareable.

Start an analysis from the "Start analyzing" button on the dashboard or the "Analyze" link in the top menu. A short wizard walks you through creating a project, uploading your data, describing what you want to know, and choosing how deep to go.

Name a new project and upload your raw data as a CSV or Excel file. The data should be in a simple tabular format, with the column names in the first row.

Diafunc accepts more than 20 file formats out of the box — tabular (CSV, TSV, Excel), columnar (Parquet, Arrow, ORC, Avro), semi-structured (JSON, JSONL, YAML, XML, HTML, PDF), archives (SQLite, zip, tar), scientific (HDF5, NetCDF), and statistical-software formats (SAS, SPSS, Stata, R RDS). Gzipped / bzipped / xz-compressed variants are unwrapped transparently. The format is detected from a combination of magic bytes, MIME type, and file extension; you can usually drag the file straight into the upload area without configuring anything.

See the full supported file formats reference for the per-format matrix, the inner-table picker behaviour for multi-table sources, and the known limitations of every importer. For dialect quirks (encodings, decimal separators, missing-value conventions, flattening nested JSON / YAML / XML) see preparing your data.

All common CSV formats should be supported automatically. To support the data import, please follow the guidelines below.

1. File extension:
   Use ".csv" as file extension to make it clear that it's a CSV file.

2. Headers:
   Include clear and consistent headers for each column into the first row to describe the data, but keep them simple.

3. Encoding:
   Save the CSV file with UTF-8 encoding to ensure compatibility with different systems and to support all characters.

4. Avoid special characters:
   Do not use special characters to avoid problems and to make the data readable.

CSV Example File

Excel files should be as plain as possible. Please follow the guidelines below to ensure the import goes smoothly.

1. File extension:
   Use ".xlsx" as file extension to make it clear that it's an Excel file.

2. Headers:
   Include clear and consistent headers for each column into the first row to describe the data, but keep them simple.

3. No formatting:
   Avoid using cell formatting options like colors, borders, or conditional formatting. Keep the text plain and simple.

4. Single sheet:
   Use only one sheet per file to keep things simple and organized.

5. No macros or pivot tables:
   Do not use any macros, pivot tables, charts or any other advanced Excel features. The data should be as raw and uncomplicated as possible.

6. Data types:
   Ensure that each column contains data of the same type (e.g., all numbers, all text). Use only text and number types. It's perfectly fine to provide numbers using the text data type.

Excel Example File

Tell Diafunc what you want to understand.

Target variables — pick one or more columns you want to explain or predict. Most analyses focus on a single target; two or three is the realistic upper end. The last column of your data is pre-selected by convention.

Questions — optionally add natural-language questions about your data (for example "Which factors drive customer churn?"). The assistant answers each question in a dedicated Questions & Answers chapter of the report, grounded in what the analysis actually found.

Pick how much compute to spend. Longer runs search deeper, and the per-hour rate grows super-linearly because deep search finds non-obvious relations that short search misses.

Preset	Time	Credits	Best for
Express	1 hour	500	Quick sanity check on a small dataset
Quick	2 hours	2 000	First-pass exploration
Standard (default)	4 hours	10 000	Most analyses of everyday datasets
Thorough	8 hours	40 000	Datasets with many columns or subtle relations
Deep	24 hours	150 000	Maximum-quality discovery on important problems

A progress bar shows percentage and elapsed time alongside a short description of what the platform is currently doing.

You can open the report notebook while the analysis is still running to peek at the chapters that have already landed. Cancelling an analysis really stops it — no credits are charged for work that hasn't completed.

The resulting analysis is a notebook inside the created project with two top-level chapters. Analysis holds everything Diafunc found and is positioned first so the reader sees the insights immediately; Foundation follows as a reference for the underlying data and the function that produced the report.

Inside Analysis the findings are organised in three layers. Data describes what is in your table, Model holds one discovered model per target together with its diagnostics, and Implications turns the results into conclusions you can act on. Above the layers sit a Headline with the strongest findings at a glance and a Concerns chapter that collects every issue the built-in detectors raised.

Discovered Relations and Questions & Answers are filled in by the platform's language model once modelling has finished, grounded in the actual facts of the analysis.

Diafunc detects what kind of prediction problem each target is — from its column type and statistical structure — and picks the matching analyzer automatically. The loss the model is trained against, the headline metrics, and the diagnostic chapters all follow the detected kind.

When you select more than one target, each target still gets its own chapter and its own formula — and the per-target models are then assembled into a single composite model that predicts all outputs at once, without any additional fitting. The composite model is the headline artifact of a multi-target analysis; the per-target chapters become its per-field breakdown, the Leaderboard chapter compares the targets side by side, and the Multi-Output chapter measures how the outputs' errors relate. The shape of the composite output is chosen automatically from your column names.

The heart of the report is the per-target Model chapter. Each model is shown as a mathematical formula alongside a plain-English restatement and the top features that drive it.

The explanations are produced by our Ufinq symbolic regression technology — the same technology that powers standalone modeling in Diafunc projects.

Ufinq

Each model chapter also shows five gauges summarising its key metrics:

Metric	Description
Quality	Overall quality of the solution, derived from a combination of complexity and error.
Complexity	Complexity of the formula — simpler is better, all else being equal.
Error	Error of the formula against the training dataset.
Time	Time spent finding this solution.
Budget	How much of the compute budget was used to find this solution.

Every model chapter carries a set of diagnostic sub-chapters that probe the formula from different angles:

Further diagnostic chapters follow the target kind. Regression targets add Prediction Interval Coverage and Regression Diagnostics; time-series targets add Temporal Diagnostics on top; count targets add a Count Distribution chapter and ordinal targets a Level Distribution chapter. Classification targets add Classification Performance (the confusion matrix), and binary targets additionally get Calibration, Classification Curves, and Decision Boundary chapters.

Classification models output a continuous model score rather than a guaranteed probability. The score estimates how likely the positive outcome is and is clamped to the range 0 to 1; the Calibration chapter — a reliability diagram with Brier score and expected calibration error — tells you whether the scores can honestly be read as probabilities.

The headline held-out metrics come with bootstrap 95% confidence intervals, so you can see how much the numbers could move on a different sample. For regression targets the prediction chart adds prediction intervals derived from the held-out residuals, and the Prediction Interval Coverage chapter checks how many held-out rows actually fall inside them.

The Data layer's General and per-variable chapters tell you how far to trust the model. Skim these first when you open a new analysis — they often reveal problems in the data that make the headline formulas misleading.

Missing Values — shows each variable's row count against the dataset total. If a column is missing more than about 30% of its values, treat any formula that relies on it with caution: the model learned from whatever rows happened to have a value, which may not be representative.

Outlier Ranges — for every numeric variable, shows the 25th, 50th, and 75th percentiles alongside the full range. A variable whose range extends far beyond the 75th percentile usually has a few extreme rows pulling formulas around. Consider whether those rows are data errors, rare-but-real events, or a signal worth modelling separately.

Class Balance (for non-numeric targets) — shows the row count per target class. If one class holds more than ~90% of the rows, a model can look accurate just by always predicting the majority class. The analysis flags this so you can judge quality correctly.

Potential Data Leaks — when a predictor correlates suspiciously strongly with a target (Pearson > 0.95), the report flags it. Very high correlations usually mean the predictor is either a direct transformation of the target, was computed from the target, or is a near-duplicate column. If a leaked variable stays in the analysis, the discovered formula will look great but fail on new data.

Segments (only if you selected a segment-by column) — the Segment Profile shows descriptive statistics grouped by segment, and the per-target Target by Segment chapters show how target values differ between groups. Look for variables whose mean, spread, or missing-value rate changes substantially between segments — those shifts are often more actionable than the aggregate formulas.

Each model chapter includes a Prediction sub-chapter with a scatter plot of actual values (x-axis) against predicted values (y-axis) for held-out rows the model never saw during training.

Points on the diagonal are accurate predictions. Vertical offsets reveal bias (the model systematically over- or under-predicts), vertical spread reveals noise (how much individual predictions vary), and points far from the diagonal are cases the model handles poorly — often the rows worth inspecting by hand.

Share — the Share button turns the project public (with confirmation) and copies a link to the report. Anyone with the link can then read it without signing in.

Ask About It — the Ask About It button opens a chat session scoped to the analysis. The assistant has the findings in context, so follow-up questions land against the specific results of your run.

Report PDF and Excel — a completed run can be downloaded as an evidence-grade PDF covering the formulas, diagnostics, concerns, and provenance, or as a single-sheet Excel workbook containing the same report bundle.

Once a configuration works well, save it as a preset: the same targets, questions, segment key, and depth preset are reused the next time you start an analysis from that preset. This is the fastest way to run the same analysis on next month's data.