nltk.classify.scikitlearn

scikit-learn (http://scikit-learn.org) is a machine learning library for Python. It supports many classification algorithms, including SVMs, Naive Bayes, logistic regression (MaxEnt) and decision trees.

This package implements a wrapper around scikit-learn classifiers. To use this wrapper, construct a scikit-learn estimator object, then use that to construct a SklearnClassifier. E.g., to wrap a linear SVM with default settings:

>>> from sklearn.svm import LinearSVC
>>> from nltk.classify.scikitlearn import SklearnClassifier
>>> classif = SklearnClassifier(LinearSVC())

A scikit-learn classifier may include preprocessing steps when it's wrapped in a Pipeline object. The following constructs and wraps a Naive Bayes text classifier with tf-idf weighting and chi-square feature selection to get the best 1000 features:

>>> from sklearn.feature_extraction.text import TfidfTransformer
>>> from sklearn.feature_selection import SelectKBest, chi2
>>> from sklearn.naive_bayes import MultinomialNB
>>> from sklearn.pipeline import Pipeline
>>> pipeline = Pipeline([('tfidf', TfidfTransformer()),
...                      ('chi2', SelectKBest(chi2, k=1000)),
...                      ('nb', MultinomialNB())])
>>> classif = SklearnClassifier(pipeline)