Tabular Data
Tabular data is a data structure organized in rows and columns, widely found in fields such as healthcare, finance, and government affairs. Its scale is usually smaller than that of image or text datasets, and its characteristics include categorical granularity and majority-class proportion.
Timeline (last 90 days)
Privacy evaluation should not by default carry over conclusions from high-dimensional datasets, but needs to be recalibrated for the characteristics of tabular data.
The total number of high-quality tabular datasets may be only around 50,000.
Tabular data is riddled with missing values, heterogeneous data types, inconsistent encoding schemes, and domain-specific noise, which distinguishes it from structured, continuous data such as images and text.
Frank Hutter believes tabular data is very dirty, which is one of the reasons it took deep learning a long time to truly handle it well.
Frank Hutter believes tabular data requires a large number of tables from diverse domains, because knowledge cannot be transferred across tables at the level of individual rows.
The ability to generalize to real, unseen datasets is the key criterion for evaluating the value of tabular deep learning methods.
The authors instantiate the framework across three data domains: binary images, tabular data, and time series.
The tabular data field is experiencing its own foundation model moment, as the paradigm of pre-training combined with in-context learning is penetrating from NLP and CV into tabular data.
Gradient boosted tree algorithms, especially XGBoost, have long been regarded as the gold standard in tabular data modeling.
All Facts (9)
Privacy evaluation should not by default carry over conclusions from high-dimensional datasets, but needs to be recalibrated for the characteristics of tabular data.
50%UnverifiedThe total number of high-quality tabular datasets may be only around 50,000.
50%UnverifiedTabular data is riddled with missing values, heterogeneous data types, inconsistent encoding schemes, and domain-specific noise, which distinguishes it from structured, continuous data such as images and text.
50%UnverifiedFrank Hutter believes tabular data is very dirty, which is one of the reasons it took deep learning a long time to truly handle it well.
50%UnverifiedFrank Hutter believes tabular data requires a large number of tables from diverse domains, because knowledge cannot be transferred across tables at the level of individual rows.
50%UnverifiedThe ability to generalize to real, unseen datasets is the key criterion for evaluating the value of tabular deep learning methods.
50%UnverifiedThe authors instantiate the framework across three data domains: binary images, tabular data, and time series.
50%UnverifiedThe tabular data field is experiencing its own foundation model moment, as the paradigm of pre-training combined with in-context learning is penetrating from NLP and CV into tabular data.
50%UnverifiedGradient boosted tree algorithms, especially XGBoost, have long been regarded as the gold standard in tabular data modeling.
50%