# Planes2D¶

2D Planes synthetic dataset.

This dataset is described in 1 and was adapted from 2. The features are generated using the following probabilities:

$P(x_1 = -1) = P(x_1 = 1) = \frac{1}{2}$
$P(x_m = -1) = P(x_m = 0) = P(x_m = 1) = \frac{1}{3}, m=2,\ldots, 10$

The target value is defined by the following rule:

$\text{if}~x_1 = 1, y \leftarrow 3 + 3x_2 + 2x_3 + x_4 + \epsilon$
$\text{if}~x_1 = -1, y \leftarrow -3 + 3x_5 + 2x_6 + x_7 + \epsilon$

In the expressions, $$\epsilon \sim \mathcal{N}(0, 1)$$, is the noise.

## Parameters¶

• seed (int) – defaults to None

Random seed number used for reproducibility.

## Attributes¶

• desc

Return the description from the docstring.

## Examples¶

>>> from river.datasets import synth

>>> dataset = synth.Planes2D(seed=42)

>>> for x, y in dataset.take(5):
...     print(list(x.values()), y)
[-1, -1, 1, 0, -1, -1, -1, 1, -1, 1] -9.07
[1, -1, -1, -1, -1, -1, 1, 1, -1, 1] -4.25
[-1, 1, 1, 1, 1, 0, -1, 0, 1, 0] -0.95
[-1, 1, 0, 0, 0, -1, -1, 0, -1, -1] -6.10
[1, -1, 0, 0, 1, 0, -1, 1, 0, 1] 1.60


## Methods¶

take

Iterate over the k samples.

Parameters

• k (int)

## References¶

1. Breiman, L., Friedman, J., Stone, C.J. and Olshen, R.A., 1984. Classification and regression trees. CRC press.