Deprecated ns. Use scicloj.metamorph.ml.rdatasets instead
Deprecated ns. Use scicloj.metamorph.ml.rdatasets instead
(breast-cancer-ds)Loads the Breast Cancer Wisconsin (Diagnostic) dataset with 30 features for binary classification.
Returns a dataset for diagnosing breast cancer from digitized images of cell nuclei. Contains 30 features describing mean, standard error, and worst values of cell characteristics (radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, fractal dimension).
Target variable is :class (:malignant or :benign, converted to numeric).
Inference target is set to :class.
Loads the Breast Cancer Wisconsin (Diagnostic) dataset with 30 features for binary classification. Returns a dataset for diagnosing breast cancer from digitized images of cell nuclei. Contains 30 features describing mean, standard error, and worst values of cell characteristics (radius, texture, perimeter, area, smoothness, compactness, concavity, concave points, symmetry, fractal dimension). Target variable is `:class` (:malignant or :benign, converted to numeric). Inference target is set to `:class`.
(diabetes-ds)Loads the Diabetes dataset with 10 features for regression.
Returns a dataset for predicting disease progression from baseline measurements.
Features include :age, :sex, :bmi (body mass index), :bp (blood pressure),
and :s1 through :s6 (six blood serum measurements).
Target variable is :disease-progression (integer). Inference target is set
to :disease-progression.
Loads the Diabetes dataset with 10 features for regression. Returns a dataset for predicting disease progression from baseline measurements. Features include `:age`, `:sex`, `:bmi` (body mass index), `:bp` (blood pressure), and `:s1` through `:s6` (six blood serum measurements). Target variable is `:disease-progression` (integer). Inference target is set to `:disease-progression`.
(iris-ds)Loads the classic Iris dataset with 4 features for multi-class classification.
Returns the famous Fisher's Iris dataset containing measurements of 150 iris
flowers from three species. Features are sepal/petal dimensions, target is
:species (setosa, versicolor, or virginica).
Species values are converted to numeric codes. Inference target is set to :species.
Loads the classic Iris dataset with 4 features for multi-class classification. Returns the famous Fisher's Iris dataset containing measurements of 150 iris flowers from three species. Features are sepal/petal dimensions, target is `:species` (setosa, versicolor, or virginica). Species values are converted to numeric codes. Inference target is set to `:species`.
(mtcars-ds)Loads the Motor Trend Car Road Tests dataset with 11 features.
Returns the classic mtcars dataset from the 1974 Motor Trend magazine, containing specifications and performance metrics for 32 automobiles. Features include mpg, cylinders, displacement, horsepower, weight, etc.
Commonly used for regression and clustering examples.
Loads the Motor Trend Car Road Tests dataset with 11 features. Returns the classic mtcars dataset from the 1974 Motor Trend magazine, containing specifications and performance metrics for 32 automobiles. Features include mpg, cylinders, displacement, horsepower, weight, etc. Commonly used for regression and clustering examples.
(sonar-ds)Loads the Sonar dataset with 60 features for binary classification.
Returns a dataset for detecting material type (metal vs. rock) from sonar
signals. Contains 60 numeric features (:x0 through :x59) representing sonar
frequency returns, with :material as the target variable.
Inference target is set to :material.
Loads the Sonar dataset with 60 features for binary classification. Returns a dataset for detecting material type (metal vs. rock) from sonar signals. Contains 60 numeric features (:x0 through :x59) representing sonar frequency returns, with `:material` as the target variable. Inference target is set to `:material`.
(titanic-ds-split)Loads the Titanic dataset pre-split into training and test sets.
Returns a map with :train and :test keys, each containing a dataset for
predicting passenger survival on the Titanic. Datasets are loaded from Nippy
format (fast binary serialization).
Use this for evaluating models with a pre-defined train/test split.
Loads the Titanic dataset pre-split into training and test sets. Returns a map with `:train` and `:test` keys, each containing a dataset for predicting passenger survival on the Titanic. Datasets are loaded from Nippy format (fast binary serialization). Use this for evaluating models with a pre-defined train/test split.
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