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vw

NAME

vw - Vowpal Wabbit -- fast online learning tool

DESCRIPTION

VW options:

-a [ --audit ]

print weights of features

-b [ --bit_precision ] arg

number of bits in the feature table

-c [ --cache ]

Use a cache. The default is <data>.cache

--cache_file arg

The location(s) of cache_file.

-d [ --data ] arg

Example Set

--daemon

read data from port 39523

--decay_learning_rate arg (=0.707106769)

Set Decay factor for learning_rate between passes

-f [ --final_regressor ] arg

Final regressor

-h [ --help ]

Output Arguments

--version

Version information

-i [ --initial_regressor ] arg

Initial regressor(s)

--initial_t arg (=1)

initial t value

--min_prediction arg

Smallest prediction to output

--max_prediction arg

Largest prediction to output

--multisource arg

multiple sources for daemon input

--noop

do no learning

--port arg

port to listen on

--power_t arg (=0)

t power value

--predictto arg

host to send predictions to

-l [ --learning_rate ] arg (=0.100000001)

Set Learning Rate

--passes arg (=1)

Number of Training Passes

-p [ --predictions ] arg

File to output predictions to

-q [ --quadratic ] arg

Create and use quadratic features

--quiet

Don't output diagnostics

-r [ --raw_predictions ] arg

File to output unnormalized predictions to

--sendto arg

send example to <hosts>

-t [ --testonly ]

Ignore label information and just test

--thread_bits arg (=0)

log_2 threads

--loss_function arg (=squared)

Specify the loss function to be used, uses squared by default. Currently available ones are squared, hinge, logistic and quantile.

--quantile_tau arg (=0.5)

Parameter \tau associated with Quantile loss. Defaults to 0.5

--unique_id arg (=0)

unique id used for cluster parallel

--compressed

use gzip format whenever appropriate. If a cache file is being created, this option creates a compressed cache file. A mixture of raw-text & compressed inputs are supported if this option is on

--sort_features

turn this on to disregard order in which features have been defined. This will lead to smaller cache sizes

--ngram arg

Generate N grams

--skip_gram arg

Generate skip grams. This in conjunction with the ngram tag can be used to generate generalized n-skip-k-gram.