movies

movies_test

movies_train


This dataset contains every film that has a Rotten Tomatoes
  rating, a RT User rating, a Metacritic score, a Metacritic User score,
  and IMDb score, and at least 30 fan reviews on Fandango. The data from
  Fandango was pulled on Aug. 24, 2015. It is licensed under CC BY 4.0


datasets

An implementation of classifier chains (CC's) for multi-label
prediction. Users can employ an external package (e.g. 'randomForest',
'C50'), or supply their own. The package can train a single set of CC's or
train an ensemble of CC's -- in parallel if running in a multi-core
environment. New observations are classified using a Gibbs sampler since
each unobserved label is conditioned on the others. The package includes
methods for evaluating the predictions for accuracy and aggregating across
iterations and models to produce binary or probabilistic classifications.

Mikhail Popov

MLPUGS

Multi-Label Prediction Using Gibbs Sampling (and Classifier
Chains)

movies function

A <code>data.frame</code> with 146 rows and 9 columns. The training data
  contains 87 movies, while the test set contains 59 movies. The first three
  columns of the training and test sets indicate the multiple labels: 1 if
  the movie got a rating equal to or greater than 80% on Metacritic,
  Rotten Tomatoes, and Fandango; and 0 otherwise.

Format

<a href="https://github.com/fivethirtyeight/data/tree/master/fandango">https://github.com/fivethirtyeight/data/tree/master/fandango</a>

Source

<a href = 'https://github.com/fivethirtyeight/data/tree/master/fandango'>https://github.com/fivethirtyeight/data/tree/master/fandango</a>

movies: FiveThirtyEight's Movie Scores

Description

Usage

Arguments

Format

Source