dataset_california_housing: Loads the California Housing dataset.

In keras3: R Interface to 'Keras'

Loads the California Housing dataset.

Description

This dataset was obtained from the StatLib repository.

It's a continuous regression dataset with 20,640 samples with 8 features each.

The target variable is a scalar: the median house value for California districts, in dollars.

The 8 input features are the following:

• MedInc: median income in block group

• HouseAge: median house age in block group

• AveRooms: average number of rooms per household

• AveBedrms: average number of bedrooms per household

• Population: block group population

• AveOccup: average number of household members

• Latitude: block group latitude

• Longitude: block group longitude

This dataset was derived from the 1990 U.S. census, using one row per census block group. A block group is the smallest geographical unit for which the U.S. Census Bureau publishes sample data (a block group typically has a population of 600 to 3,000 people).

A household is a group of people residing within a home. Since the average number of rooms and bedrooms in this dataset are provided per household, these columns may take surprisingly large values for block groups with few households and many empty houses, such as vacation resorts.

Usage

dataset_california_housing(
  version = "large",
  path = "california_housing.npz",
  test_split = 0.2,
  seed = 113L
)

Arguments

version	"small" or "large". The small version contains 600 samples, the large version contains 20,640 samples. The purpose of the small version is to serve as an approximate replacement for the deprecated boston_housing dataset.
path	path where to cache the dataset locally (relative to Sys.getenv("KERAS_HOME")).
test_split	fraction of the data to reserve as test set.
seed	Random seed for shuffling the data before computing the test split.

Value

Nested list of arrays: ⁠(x_train, y_train), (x_test, y_test)⁠.

x_train, x_test: arrays with shape ⁠(num_samples, 8)⁠ containing either the training samples (for x_train), or test samples (for y_train).

y_train, y_test: arrays of shape (num_samples) containing the target scalars. The targets are float scalars typically between 25,000 and 500,000 that represent the home prices in dollars.

See Also

Other datasets:
dataset_boston_housing()
dataset_cifar10()
dataset_cifar100()
dataset_fashion_mnist()
dataset_imdb()
dataset_mnist()
dataset_reuters()

keras3 documentation built on June 8, 2025, 1:36 p.m.