fetch_growth#

skfda.datasets.fetch_growth(*, return_X_y: typing_extensions.Literal[False] = False, as_frame: bool = False) Bunch[source]#
skfda.datasets.fetch_growth(*, return_X_y: typing_extensions.Literal[True], as_frame: typing_extensions.Literal[False] = False) Tuple[FDataGrid, ndarray[Any, dtype[int64]]]
skfda.datasets.fetch_growth(*, return_X_y: typing_extensions.Literal[True], as_frame: typing_extensions.Literal[True]) Tuple[DataFrame, Series]

Load the Berkeley Growth Study dataset.

The data is obtained from the R package ‘fda’, which takes it from the Berkeley Growth Study.

The Berkeley Growth Study (Tuddenham and Snyder, 1954) recorded the heights of 54 girls and 39 boys between the ages of 1 and 18 years. Heights were measured at 31 ages for each child, and the standard error of these measurements was about 3mm, tending to be larger in early childhood and lower in later years.

References

Tuddenham, R. D., and Snyder, M. M. (1954) “Physical growth of California boys and girls from birth to age 18”, University of California Publications in Child Development, 1, 183-364.

Parameters:
  • return_X_y – Return only the data and target as a tuple.

  • as_frame – Return the data in a Pandas Dataframe or Series.

Examples using skfda.datasets.fetch_growth#

Classification methods

Classification methods

Depth based classification

Depth based classification

Elastic registration

Elastic registration

Functional Principal Component Analysis

Functional Principal Component Analysis

K-nearest neighbors classification

K-nearest neighbors classification

Representation of functional data

Representation of functional data

Introduction

Introduction

Getting the data

Getting the data

Scikit-fda and scikit-learn

Scikit-fda and scikit-learn