Data Science Vocabulary

Creating new input variables from raw data

“Feature engineering extracted meaningful signals from the timestamp data.”

Origin: From Latin `factura` (a making) + Old French `engin` (skill), from Latin `ingenium` (cleverness)

Reducing the number of variables while preserving information

“Dimensionality reduction made the dataset manageable for visualization.”

Origin: From Latin `dimensio` (a measuring), from `dimetiri` (to measure out)

Evaluating models by training on subsets and testing on the rest

“Cross-validation revealed the model's true generalization performance.”

Origin: From Latin `crux` (cross) + `validus` (strong, effective), from `valere` (to be strong)

The proportion of positive predictions that are correct

“High precision means few false positives in our spam detection.”

Origin: From Latin `praecisio` (a cutting off), from `praecidere` (to cut off), from `prae-` (before) + `caedere` (to cut)

The proportion of actual positives correctly identified

“High recall ensures we catch most fraudulent transactions.”

Origin: From Latin `re-` (again, back) + `calare` (to call, summon)

The harmonic mean of precision and recall

“The F1 score balances precision and recall into a single metric.”

Origin: Named `F1` as the first F-score or F-measure; `F` from F-measure, a weighted harmonic mean

A graph showing classifier performance at various thresholds

“The ROC curve demonstrated excellent discrimination between classes.”

Origin: Acronym for `Receiver Operating Characteristic`, from signal detection theory in 1940s

Area Under the Curve, measuring overall model performance

“An AUC of 0.95 indicates excellent predictive ability.”

Origin: Acronym from Latin `area` (open space) + Old English `under` + Latin `curvus` (bent)

A table showing prediction results versus actual values

“The confusion matrix revealed the model confused cats with dogs.”

Origin: From Latin `confusio` (mixing together) + `matrix` (womb, breeding female), from `mater` (mother)

The balance between underfitting and overfitting

“Understanding the bias-variance tradeoff guides model complexity decisions.”

Origin: From French `biais` (slant) + Latin `variare` (to change) + Old English `tredan` (to tread) + `of` (away)

Techniques to prevent overfitting by penalizing complexity

“Regularization prevented the model from memorizing training data.”

Origin: From Latin `regula` (rule, straight piece of wood) + `-ization` suffix

Scaling data to a standard range

“Normalization ensured all features contributed equally to the model.”

Origin: From Latin `norma` (carpenter`s square, rule) + `-ization' suffix

Filling in missing data values

“Mean imputation replaced missing values with column averages.”

Origin: From Latin `imputare` (to reckon, charge), from `in-` (in) + `putare` (to reckon, think)

Identifying data points that differ significantly from others

“Outlier detection flagged suspicious transactions for review.”

Origin: From `out` (Old English `ut`) + `lie` (Old English `licgan`) + Latin `detectio` (uncovering)

Grouping similar data points together

“Clustering revealed three distinct customer segments.”

Origin: From Old English `cluster` (bunch, group), related to `clot`

Predicting which category a data point belongs to

“Classification determines whether an email is spam or legitimate.”

Origin: From Latin `classis` (class, division) + `facere` (to make)

Predicting a continuous numerical value

“Regression models forecast next quarter's sales figures.”

Origin: From Latin `regredi` (to go back), from `re-` (back) + `gradi` (to step, walk)

Data points indexed in time order

“Time series analysis detected seasonal patterns in demand.”

Origin: From Old English `tima` (time) + Latin `series` (row, chain), from `serere` (to join)

Identifying unusual patterns that don't conform to expected behavior

“Anomaly detection caught the security breach within minutes.”

Origin: From Greek `anomalia` (unevenness), from `an-` (not) + `homalos` (even)

Extract, Transform, Load - the data pipeline process

“The ETL pipeline processes millions of records nightly.”

Origin: Acronym from Latin `extractus` (drawn out) + `transformare` (change shape) + Old English `hladan` (to load)

A sequence of processes

“The data pipeline cleans and transforms the raw input.”

Origin: From `pipe` (Old English `pipe` from Latin `pipare` to chirp) + `line` (Latin `linea`)

The representation of an object, situation, or set of information as a chart or other image

“Data visualization helps in identifying trends.”

Origin: From Latin `visualis` (of sight), from `visus` (sight), from `videre` (to see)