Skip to main content
This component is currently in beta and may be subject to changes or instability. The Distribution Profile component answers the question: what is the shape of each column’s data distribution?

Use cases

  • Fraud detection preprocessing — Is the transaction amount column heavy-tailed enough to warrant a log transformation, or is a Normal assumption reasonable?
  • Sensor data validation — Are the readings from a temperature sensor unimodal as expected, or does a second peak suggest a recurring calibration fault?
  • Feature engineering — Which columns are highly skewed and would benefit from normalisation before being fed into a model?

What it computes

  • Skewness and kurtosis: measures of distributional shape — how lopsided and how heavy-tailed each numeric column is.
  • Best-fit parametric distribution: the best-fitting distribution from a candidate set, along with its fitted parameters.
  • Shapiro-Wilk normality test: a pp-value testing whether each numeric column is normally distributed.
  • Modality: whether each numeric column’s distribution is unimodal, bimodal, or multimodal.
  • Kernel density estimate (KDE): a smooth estimate of each numeric column’s probability density.
  • Histograms: a 30-bin histogram for each numeric column, and a proportion histogram for categorical columns with 10 or fewer unique categories.

Display

The Distribution Profile component displays the following in the Catalog.
  • Distribution profiles table: skewness, kurtosis, best-fit distribution and its parameters, Shapiro-Wilk pp-value, modality, and top peaks — one row per numeric column.
  • Fitted distributions: all candidate parametric distributions ranked by BIC, with fitted parameters and goodness-of-fit pp-value — one table per numeric column.
  • Numeric histograms: a 30-bin histogram per numeric column.
  • Categorical histograms: a proportion histogram per low-cardinality categorical column (10 or fewer unique categories).
  • Kernel density estimates: a smooth density curve per numeric column.

Inputs

Output shape

Filtering from chat

Summand can query the distribution profile artifact in a variety of ways:

Compute profile