Attributes

The attributes this instance offers: what each is for, the questions it answers and when to reach for something else. Operators are sorted by name; each name links to its detail block below.

Operators

ATTR_CODE_IN

Adds a 1 / 0 column saying whether each row’s code is one of a listed set, keeping every row in the base.

Level: basic

Questions it answers:

• What share of all respondents gave a top-two-box answer (codes 4 or 5)?
• Which orders carry one of the priority status codes, as a column to average by region?

Use cases by domain:

• survey: Top-box flag to average as a share of the whole weighted base: non-answers stay in the denominator unless FILTER_NULL drops them first.
• ops: Flag for orders whose status code is in a chosen group.

Assumptions:

• On a categorical field a code is a dictionary label; one absent from the dictionary matches nothing.
• A row with a missing value reads 0, the same as a row with another code.

Use something else:

• FILTER_INCLUDE when you want to keep only the rows with those codes.
• AGG_FREQUENCY when you want how often a single value occurs.
• ATTR_SET_HAS when the field is a multi-select.

Glossary: missing-value

Skill: op-attr-code-in

ATTR_DATE_PART

Adds a calendar part of a date or timestamp to every row, such as the year, the month, a year-month like 202403 or the hour.

Level: basic

Questions it answers:

• Which month of the year does each order fall in, so seasons can be compared across years?
• What year-month label should each response carry for a monthly breakdown?

Use cases by domain:

• survey: Tag each response with its fieldwork year-month.
• ops: Month-of-year column for comparing seasonal order volume.
• science: Collection year per sample for a by-year breakdown.

Assumptions:

• Reads a date field (epoch days) or a datetime field; a datetime is read on the local clock of its time zone, and the hour part needs a datetime.
• The part is an encoded number (YYYYMM for year_month), so arithmetic on it is meaningless.
• A missing date reads 0.

Use something else:

• GROUP_DATE when you only want rows grouped by day, week, month or year.
• FEAT_DATE_FEATURES when you want several calendar columns at once, including weekday.

Glossary: missing-value

Skill: op-attr-date-part

ATTR_FACTOR_SCORE

Adds to every row its score on one factor behind several related numeric fields: an estimate of the hidden trait they measure together.

Level: advanced

Also known as: factor score, regression factor score, Thurstone score, Bartlett score, latent score

Questions it answers:

• What is each respondent’s score on the satisfaction factor behind these rating questions?
• Can I score every row on the two constructs a factor analysis found, to compare segments on them?

Use cases by domain:

• survey: Score each respondent on the constructs an attitude battery measures, then crosstab or test the scores by segment.
• science: Estimate each subject’s standing on a latent trait from several noisy indicators before a later model.

Assumptions:

• The factors come from one fit over every row that passed the filters (or the subset fit_on selects), never per group; every filtered row is scored.
• The fit is MAT_FACTOR’s with the same settings, so factor numbers and directions match its loadings: read them before naming a score.
• A row missing any of the fields is left out of the fit and gets no score (listwise deletion).
• There must be more complete rows than fields, the factor count must be identified, and no field may be constant or a sum of the others; otherwise every score is empty, with a warning.

Use something else:

• MAT_FACTOR when you want how many factors there are and which fields load on each.
• ATTR_PC_SCORE when you want a summary of all the fields’ variance rather than a common trait.

Glossary: factor, loading, correlation, z-score, listwise-deletion

Skill: op-attr-factor-score

ATTR_FORMULA

Adds a number to every row computed from the row’s own fields with an expression, such as price * qty.

Level: intermediate

Questions it answers:

• What is each order’s line total from its price and quantity?
• Which rows meet a combined rule, as a 0/1 column for counting?

Use cases by domain:

• survey: Sum of several rating items into one score per respondent.
• ops: Margin per order from revenue and cost.
• science: Body-mass index from recorded weight and height.

Assumptions:

• A missing field enters the expression as nil, and an operator that cannot take nil fails the request. A guard such as x ?? 0 turns each missing value into a real 0, which pulls averages down, so prefer dropping those rows with FILTER_NULL or pick a fallback that means something.
• True / false results become 1 / 0.
• It cannot read another attribute’s column from the same request.

Use something else:

• FILTER_EXPRESSION when you want to keep or drop rows by a rule rather than add a column.
• FEAT_LOG when you want the logarithm of a field.

Glossary: missing-value

Skill: op-attr-formula

ATTR_MAHALANOBIS

Adds to every row how unusual its mix of several numeric fields is, allowing for how those fields usually vary together.

Level: advanced

Also known as: mahalanobis distance, multivariate outlier score, d-squared

Questions it answers:

• Which respondents gave an unusual pattern of answers across the whole battery, even if no single answer is extreme?
• Which transactions have an odd combination of amount, frequency and basket size?

Use cases by domain:

• survey: Flag straight-liners and odd answer patterns across a rating battery before analysis.
• ops: Screen orders whose size, discount and delivery time together look unlike the rest.
• science: Find samples whose set of measurements is unusual as a whole.

Assumptions:

• The typical row and the spread come from one fit over every row that passed the filters (or the subset fit_on selects), never per group; every filtered row is scored.
• A row missing any of the fields is left out of the fit and gets no score (listwise deletion).
• The p output reads the score against a chi-square curve, which holds only when the fields are roughly jointly normal.
• There must be more complete rows than fields, and no field may be constant or a sum of the others; otherwise every score is empty, with a warning.

Use something else:

• ATTR_ZSCORE when only one field matters.
• TEST_HOTELLING_T2 when you want how far apart groups are, not single rows.

Glossary: distance, outlier, covariance, mean, listwise-deletion, chi-square, p-value, normal-distribution, degrees-of-freedom

Skill: op-attr-mahalanobis

ATTR_NORMALIZED

Rescales a numeric field to 0 to 1 on every row: 0 is the smallest value, 1 the largest.

Level: basic

Questions it answers:

• How can fields on very different scales be put on one 0-to-1 scale before combining them?
• Where does each row’s value sit between the lowest and highest seen?

Use cases by domain:

• survey: Rescale items to 0-1 by their observed lowest and highest answers, before comparing their spread.
• ops: Scale each site’s load between its observed minimum and maximum.
• harness: Bound an input to 0..1 before handing it to a scoring model.

Assumptions:

• Computed over every row that passed the filters, before grouping: a group never re-centres it.
• It uses the OBSERVED lowest and highest values, not a scale’s endpoints: if nobody answered 1 on a 1-5 item, 2 maps to 0, so the same raw score lands at different positions across items, filtered subsets or waves.
• A missing value reads 0, the same as the minimum, as does every row when all values are equal; in a composite that pulls the score down.

Use something else:

• ATTR_PERCENTILE when the field has extreme values that would squeeze the rest of the range.
• ATTR_ZSCORE when you want distance from the mean in standard deviations.
• ATTR_FORMULA when you want a composite on the scales’ fixed endpoints (1-5, 0-10), such as (x - 1) / 4.

Glossary: outlier, missing-value

Skill: op-attr-normalized

ATTR_PC_SCORE

Adds to every row its score on one principal component of several related numeric fields: one number standing in for what they share.

Level: advanced

Also known as: principal component score, pc score, component score, index score

Questions it answers:

• What is each respondent’s score on the main dimension behind these rating questions?
• Can I replace these five correlated measures with one summary column for a later model?

Use cases by domain:

• survey: Score each respondent on the leading dimension of an attitude battery, to segment or chart by it.
• science: Reduce many correlated measurements of each specimen to a few component columns before further analysis.

Assumptions:

• The components come from one fit over every row that passed the filters (or the subset fit_on selects), never per group; every filtered row is scored.
• A component’s direction (which end is high) follows MAT_PCA’s rule, not the meaning of the fields: read the loadings before naming a score.
• A row missing any of the fields is left out of the fit and gets no score (listwise deletion).
• There must be more complete rows than fields, and no field may be constant or a sum of the others; otherwise every score is empty, with a warning.

Use something else:

• MAT_PCA when you want how many dimensions there are and which fields load on each.
• ATTR_MAHALANOBIS when you want how unusual a row is across the fields.

Glossary: principal-component, eigenvalue, loading, correlation, covariance, z-score, listwise-deletion

Skill: op-attr-pc-score

ATTR_PERCENTILE

Adds to every row its percentile rank, rank / n * 100: the share of rows at or below it when values are untied.

Level: basic

Questions it answers:

• Where does each store’s revenue rank among all stores, as a percentage?
• Which respondents are in the top tenth for spend?

Use cases by domain:

• survey: Percentile of each respondent’s household income.
• ops: Percentile rank of each order’s delivery time.
• science: Percentile of each sample’s reading within the batch.

Assumptions:

• Computed over every row that passed the filters, before grouping: a group never re-centres it.
• Tied values do not share a percentile: each tied row takes its own rank in an arbitrary order, so equal values can read tens of points apart.
• Rows tied at a cut-off are split across it arbitrarily and can change between runs; cut on the value (AGG_PERCENTILE, then a filter) for a reproducible top tenth.
• Not streamable: every value is sorted before the first row is ready.

Use something else:

• AGG_PERCENTILE when you want the value at a chosen percentile, such as the 90th.
• WIN_RANK when you want ranks within partitions, or tied values to share a rank.
• GROUP_QUANTILE when you want rows split into equal-sized bands.

Glossary: percentile, rank, ties

Skill: op-attr-percentile

ATTR_PROFILE_SIMILARITY

Adds to every row how closely its related fields match a target profile: an ideal pattern, a benchmark, or its own segment target.

Level: intermediate

Also known as: profile similarity, profile match, pattern similarity, fit to target

Questions it answers:

• How closely does each respondent’s brand ratings follow the ideal loyalist profile?
• How similar is each store’s channel mix to its region’s target mix?

Use cases by domain:

• survey: Score each respondent’s fit to an ideal rating profile, the pattern rather than the level (correlation).
• ops: Compare each account’s spend mix with the target mix for its segment, kept in a registered reference table.

Assumptions:

• The target is matched to the fields by label, never by position; a target whose labels differ is refused before any row is read.
• Each row is compared from its own values only; a row weight does not apply.
• A field missing on a row drops that pair; a row with fewer complete pairs than min_valid gets no value.
• A row whose segment has no target profile gets no value and is counted, rather than refusing the run.

Use something else:

• ATTR_VEC_DISTANCE when you want how far apart the values are rather than how alike the pattern is.
• ATTR_MAHALANOBIS when you want how unusual a row is against the whole cohort.
• MAT_DISTANCE when you want how far apart whole groups are.

Glossary: similarity, correlation, pairwise-deletion

Skill: op-attr-profile-similarity

ATTR_REG_FITTED

Adds to every row the value a straight-line model predicts for its target from its predictors.

Level: advanced

Questions it answers:

• What order value would we expect for each customer given their visits and tenure?
• Which stores sell above or below what their size and footfall predict?

Use cases by domain:

• survey: Expected satisfaction per respondent from their attribute ratings.
• ops: Expected handling time per ticket from queue length and agent tenure.
• science: Expected response per subject from dose and body weight.

Assumptions:

• The model is fitted to the rows that passed the filters and have the target and every predictor (listwise deletion).
• Each ATTR_REG_* slot fits its own straight-line model; the request’s regressions are not reused.
• A prediction reflects association in these rows, never what would happen if a predictor were changed.
• A row missing any predictor reads 0, which is not a prediction.

Use something else:

• REG_OLS when you want the coefficients, R-squared and p-values of the model.
• ATTR_REG_RESIDUAL when you want how far each row sits from its prediction.
• REG_GLM when the target is yes/no or a count.

Glossary: regression-coefficient, overfitting, listwise-deletion

Skill: op-attr-reg-fitted

ATTR_REG_LEVERAGE

Adds to every row its leverage: how unusual its predictor values are, and so how hard it can pull a straight-line fit.

Level: advanced

Questions it answers:

• Which rows have predictor values so far from the rest that they could steer the model?
• Which customers have predictor values extreme enough that they could pull the fitted line (read their residuals to see whether they do)?

Use cases by domain:

• ops: Find the few very large accounts that could dominate a revenue model; read their residuals before concluding they do.
• science: Screen subjects with extreme dose or weight before trusting a fit.

Assumptions:

• The model is fitted to the rows that passed the filters and have the target and every predictor (listwise deletion).
• Each ATTR_REG_* slot fits its own straight-line model; the request’s regressions are not reused.
• Unpenalized least squares only: any penalty is refused.
• It looks at the predictors only, never at the target.

Use something else:

• ATTR_REG_RESIDUAL when you want rows the model predicts badly.
• REG_OLS when you want the model itself.

Glossary: outlier, residual, listwise-deletion

Skill: op-attr-reg-leverage

ATTR_REG_PREDICT

Adds to every row the value a model fitted once, elsewhere in the request, predicts for it from the row’s own predictor values.

Level: advanced

Questions it answers:

• How close are the model’s predictions on weeks it was not fitted to (a holdout)?
• How much of each week’s sales does the TV term of the fitted model account for?

Use cases by domain:

• survey: Score this wave’s respondents with the satisfaction model fitted on last wave.
• ops: Predict handling time for new tickets from a model fitted on last quarter’s.
• science: Predict the held-out subjects’ response from the dose model fitted on the rest.

Assumptions:

• The model is fitted once, on the fit request’s own rows, before any slot runs; scoring never refits.
• The scoring rows must carry every predictor column the fit used, built the same way (same features); an absent one-hot column is the reference level.
• A row missing any predictor the score reads gets no value (null), never 0.
• A prediction reflects association in the fitted rows, never what would happen if a predictor were changed.

Use something else:

• ATTR_REG_FITTED when the model should be fitted to these same rows inside the slot.
• REG_OLS when you want the coefficients, their standard errors and p-values.

Glossary: regression-coefficient, overfitting, link-function, missing-value

Skill: op-attr-reg-predict

ATTR_REG_RESIDUAL

Adds to every row its residual: the actual target value minus the value a straight-line model predicts for it.

Level: advanced

Questions it answers:

• Which stores sell far more or less than their size and footfall predict?
• Do the model’s misses grow with the predicted value, or bend in a curve?

Use cases by domain:

• survey: Respondents far more or less satisfied than their ratings predict.
• ops: Tickets that took far longer than queue length and tenure predict.
• science: Check a dose-response fit for curvature or growing spread.

Assumptions:

• The model is fitted to the rows that passed the filters and have the target and every predictor (listwise deletion).
• Each ATTR_REG_* slot fits its own straight-line model; the request’s regressions are not reused.
• A row missing the target or any predictor reads 0, the same as a row the model fits exactly.

Use something else:

• REG_OLS when you want the model’s summary (coefficients, R-squared).
• ATTR_REG_LEVERAGE when you want how unusual each row’s predictor values are.
• ATTR_ZSCORE when you want how far a value is from the mean, with no model.

Glossary: residual, heteroscedasticity, outlier, listwise-deletion

Skill: op-attr-reg-residual

ATTR_SCALE_SCORE

Adds to every row its score on a rating battery: the average of its answered items or a prorated total, reverse-worded items flipped.

Level: intermediate

Also known as: scale score, summated rating scale, composite score

Questions it answers:

• What is each respondent’s satisfaction score across the five satisfaction items, counting only people who answered at least four?
• What is each patient’s symptom total when two of the items are worded the other way round?

Use cases by domain:

• survey: Turn an agree-disagree battery into one score per respondent, flipping the negatively worded items.
• science: Score a questionnaire instrument per participant, prorating the total when an item is skipped.

Assumptions:

• Each row is scored from its own answers only; no other row changes it, and a row weight does not apply.
• A skipped item is left out, not counted as 0: the sum is the average of the answered items times the item count.
• A row with fewer answered items than min_valid gets no score rather than a low one.
• Flipping an item needs the scale’s true range, which is declared and never guessed from the data; an answer outside it is treated as skipped and reported.

Use something else:

• MAT_RELIABILITY when you want how consistently the items measure one thing.
• ATTR_FACTOR_SCORE when the items should be weighted by how much each reflects a common trait.
• ATTR_FORMULA when you want an arbitrary formula of the items.

Glossary: mean, missing-value, reliability

Skill: op-attr-scale-score

ATTR_SET_HAS

Adds a 1 / 0 column saying whether each row’s multi-select field includes one named option.

Level: basic

Questions it answers:

• Which respondents ticked ‘price’ among their reasons, as a column to cross with other answers?
• Which tickets carry the ‘urgent’ tag?

Use cases by domain:

• survey: Flag for one option of a multiple-choice question, to average as a share among those who answered: drop missing answers with FILTER_NULL first, since they read 0.
• ops: Flag for orders tagged with a given promotion.

Assumptions:

• The option must be in the field’s dictionary; an unknown one is refused.
• A row with a missing value reads 0, the same as a row that did not choose the option.

Use something else:

• FILTER_SET_CONTAINS_ANY when you want to keep only rows that include any of several options.
• AGG_SET_FREQUENCY when you want how often each option was chosen.

Glossary: missing-value

Skill: op-attr-set-has

ATTR_SET_POPCOUNT

Adds to every row the number of options its multi-select field has selected.

Level: basic

Questions it answers:

• How many reasons did each respondent tick?
• Which customers selected three or more interests?

Use cases by domain:

• survey: Number of brands each respondent is aware of.
• ops: Number of tags on each support ticket.

Assumptions:

• An empty selection reads 0, and so does a row with a missing value.

Use something else:

• AGG_SET_CARDINALITY_AVG when you want the average number of selections per group.
• ATTR_SET_HAS when you want whether one particular option was chosen.

Glossary: missing-value

Skill: op-attr-set-popcount

ATTR_TSCORE

Adds to every row its T-score: the z-score rescaled so the mean is 50 and one standard deviation is 10.

Level: intermediate

Questions it answers:

• How does each candidate’s test result compare with the group, on a 50-centred scale?
• Which respondents score more than one standard deviation above average?

Use cases by domain:

• survey: Report standardized scale scores on a 50-centred scale, where negative values are rare.
• science: Express assessment scores on a mean-50, SD-10 scale relative to the rows analysed (not to a published norm group).

Assumptions:

• Computed over every row that passed the filters, before grouping: a group never re-centres it.
• Uses the population standard deviation (dividing by n).
• A missing value reads 50, as does every row when all values are equal.

Use something else:

• ATTR_ZSCORE when you want plain standard-deviation units centred on 0.
• ATTR_PERCENTILE when you want each row’s percentile rank (the share at or below it when values are untied).

Glossary: z-score, standard-deviation, mean

Skill: op-attr-tscore

ATTR_VEC_DISTANCE

Adds to every row how far its set of related fields lies from a target profile, optionally in units of each field’s spread.

Level: intermediate

Also known as: distance to target, gap to benchmark, profile distance, standardized distance

Questions it answers:

• How far is each store’s KPI profile from the benchmark store, with every KPI on its own scale?
• Which respondents’ ratings sit furthest from the target profile?

Use cases by domain:

• ops: Rank stores by their standardized gap to a benchmark KPI profile.
• science: Measure each specimen’s distance from a reference composition.

Assumptions:

• The target is matched to the fields by label, never by position; a target whose labels differ is refused before any row is read.
• A field missing on a row drops that pair and the sum is scaled up for the dropped pairs; a row with fewer complete pairs than min_valid gets no value.
• The standardized and covariance-based distances learn each field’s spread from one fit over the filtered rows (weighted when a weight is set), never per group; the plain ones read each row alone.
• A row whose segment has no target profile gets no value and is counted, rather than refusing the run.

Use something else:

• ATTR_PROFILE_SIMILARITY when you want how alike the pattern is rather than how far apart the values are.
• ATTR_MAHALANOBIS when the target is the cohort’s own centre.
• MAT_DISTANCE when you want how far apart whole groups are.

Glossary: distance, standard-deviation, covariance, pairwise-deletion

Skill: op-attr-vec-distance

ATTR_ZSCORE

Adds to every row its z-score: how many standard deviations the row’s value sits above or below the mean.

Level: intermediate

Also known as: standard score, standardized score

Questions it answers:

• Which orders are unusually large compared with all orders?
• How do scores measured on different scales compare once put on one footing?

Use cases by domain:

• survey: Put rating items with different spreads on a common scale before combining them.
• ops: Flag deliveries far slower than typical for review.
• science: Standardize a measurement before comparing it with another.

Assumptions:

• Computed over every row that passed the filters, before grouping: a group never re-centres it.
• Uses the population standard deviation (dividing by n).
• A missing value reads 0, as does every row when all values are equal.

Use something else:

• AGG_ZSCORE when you want the centre and spread of each group, not a score per row.
• ATTR_PERCENTILE when the field is skewed or has extreme values and you want a position they cannot distort.
• OVERLAY_ZSCORE_VS_TOTAL when you want each group’s value against all groups.

Glossary: z-score, standard-deviation, mean, outlier

Skill: op-attr-zscore