summary_stats

Summary statistics for the all datasets

datasets

Check available classification and regression data sets from the PMLB repository and download them.
The PMLB repository (<https://github.com/EpistasisLab/pmlbr>) contains a curated collection of data sets for evaluating and comparing machine learning algorithms.
These data sets cover a range of applications, and include binary/multi-class classification problems and
regression problems, as well as combinations of categorical, ordinal, and continuous features.
There are currently over 150 datasets included in the PMLB repository.

Trang Le

pmlbr

Interface to the Penn Machine Learning Benchmarks Data
Repository

makeyourownmaker 

Jason Moore

University of Pennsylvania 

summary_stats function

A data frame with 10 variables:<dl>
 <dt>dataset:</dt>
<dd>Dataset name</dd> <dt>n_instances:</dt>
<dd>Number of data observations (equal to number of rows)</dd> <dt>n_features:</dt>
<dd>Total number of features (number of columns - 1)</dd> <dt>n_binary_features:</dt>
<dd>Number of binary features</dd> <dt>n_categorical_features:</dt>
<dd>Number of categorical features</dd> <dt>n_continuous_features:</dt>
<dd>Number of continuous features</dd> <dt>n_classes:</dt>
<dd>Number of classes in target variable</dd> <dt>endpoint_type:</dt>
<dd>Value type of endpoint/target (can be binary, categorical or continuous)</dd> <dt>imbalance:</dt>
<dd>Imbalance metric, where zero means that the dataset is perfectly balanced and the higher the value, the more imbalanced the dataset</dd> <dt>task:</dt>
<dd>Type of problem/task. Can be classification or regression.</dd>
</dl>

Format

Summary statistics for the all datasets — summary_stats

A data frame with 10 variables:<dl>
 <dt>dataset:</dt>
<dd>Dataset name</dd>

 <dt>n_instances:</dt>
<dd>Number of data observations (equal to number of rows)</dd>

 <dt>n_features:</dt>
<dd>Total number of features (number of columns - 1)</dd>

 <dt>n_binary_features:</dt>
<dd>Number of binary features</dd>

 <dt>n_categorical_features:</dt>
<dd>Number of categorical features</dd>

 <dt>n_continuous_features:</dt>
<dd>Number of continuous features</dd>

 <dt>n_classes:</dt>
<dd>Number of classes in target variable</dd>

 <dt>endpoint_type:</dt>
<dd>Value type of endpoint/target (can be binary, categorical or continuous)</dd>

 <dt>imbalance:</dt>
<dd>Imbalance metric, where zero means that the dataset is perfectly balanced and the higher the value, the more imbalanced the dataset</dd>

 <dt>task:</dt>
<dd>Type of problem/task. Can be classification or regression.</dd>


</dl>

summary_stats: Summary statistics for the all datasets

Description

Usage

Arguments

Format