mkdata

Generate synthetic datasets

Install

• npm i mkdata -S or
• npm i mkdata -g or run using npx without installation
• npx mkdata -d moons -s 1000 -o friedman1.csv

CLI

mkdata -d friedman1 -s 1000 -o friedman1.csv

or using stdout

mkdata -d friedman1 -s 1000 > friedman1.csv

Params:

• -d, --dataset - dataset name
• -f, --nFeatures - number of features
• -s, --nSamples - number of samples
• -n, --noise - noise size
• -o, --output - output file name
• -r, --randomState - seed

API

const make = require('mkdata')
const [X, y] = make.spirals({ nSamples: 1000 })

friedman1, friedman2, friedman3 methods also return data generating functions:

const [X, y, f] = make.friedman3({ nSamples: 1000 })
const yt = X.map(f)

Datasets

• Friedman 1 (y = 10 * sin(Pi * x1 * x2) + 20 * (x3 - 0.5) ** 2 + 10 * x4 + 5 * x5 + e)
• Friedman 2 (y = sqrt(x1 ** 2 + (x2 * x3 - 1 / (x2 * x4)) ** 2) + e)
• Friedman 3 (y = atan(x2 * x3 - 1 / (x2 * x4) / x1) + e)
• Hastie (binary classification problem used in Hastie et al 2009)
• Moons (two interleaving half circles)
• Peak (peak benchmark problem)
• Ringnorm (from Breiman 1996)
• Spirals (two entangled spirals)
• Swissroll (from S. Marsland 2009)

Web demo

Generate same synthetic datasets without installing mkdata here: https://statsim.com/gen/ (CSV format)