dataset_california_housing {keras3} | R Documentation |
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 |
|
path |
path where to cache the dataset locally
(relative to |
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()