Welcome to mlxtend's documentation! Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks.

In rare cases, users reported problems on certain systems with the default pip installation command, which installs mlxtend from the binary distribution ("wheels") on PyPI.

The generate_rules takes dataframes of frequent itemsets as produced by the apriori, fpgrowth, or fpmax functions in mlxtend.association. To demonstrate the usage of the generate_rules …

Each itemset in the 'itemsets' column is of type frozenset, which is a Python built-in type that behaves similarly to sets except that it is immutable (For more info, see …

from mlxtend.frequent_patterns import apriori Overview Apriori is a popular algorithm [1] for extracting frequent itemsets with applications in association rule learning. The apriori algorithm …

Via the fit method, the TransactionEncoder learns the unique labels in the dataset, and via the transform method, it transforms the input dataset (a Python list of lists) into a one-hot encoded …

Since mlxtend v0.21.0, it is possible to specify feature groups. Feature groups allow you to group certain features together, such that they are always selected as a group.

Each entry in the "antecedents" and "consequents" columns are of type frozenset, which is a Python built-in type that behaves similarly to sets except that it is immutable (For more info, …

Since mlxtend v0.21.0, it is possible to specify feature groups. Feature groups allow you to group certain features together, such that they are always selected as a group.

n_jobs : int or None, optional (default=None) The number of CPUs to use to do the computation using Python's multiprocessing library. None means 1. -1 means using all processors. New in …