Random Multinomial Logit
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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In statistics and machine learning, random multinomial logit (RMNL) is a technique for (multi-class) statistical classification using repeated multinomial logit analyses via Leo Breiman''s random forests.Several learning algorithms have been proposed to handle multiclass classification. While some algorithms are extensions or combinations of intrinsically binary classification methods (e.g., multiclass classifiers as one-versus-one or one-versus-all binary…mehr

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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In statistics and machine learning, random multinomial logit (RMNL) is a technique for (multi-class) statistical classification using repeated multinomial logit analyses via Leo Breiman''s random forests.Several learning algorithms have been proposed to handle multiclass classification. While some algorithms are extensions or combinations of intrinsically binary classification methods (e.g., multiclass classifiers as one-versus-one or one-versus-all binary classifiers), other algorithms like multinomial logit (MNL) are specifically designed to map features to a multiclass output vector. MNL''s stability has a proven track record in many disciplines, including transportation research and CRM (customer relationship management).