If set, classifier capabilities are not checked before classifier is built
(use with caution).
-stopwords-handler
The stopwords handler to use (default Null).
M
Minimum word frequency. Words with less than this frequence are ignored.
If periodic pruning is turned on then this is also used to determine which
words to remove from the dictionary (default = 3).
default: 3.0
P
How often to prune the dictionary of low frequency words (default = 0, i.e. don't prune)
default: 0
W
Use word frequencies instead of binary bag of words.
batch-size
The desired batch size for batch prediction (default 100).
lnorm
Specify L-norm to use (default 2.0)
default: 2.0
lowercase
Convert all tokens to lowercase before adding to the dictionary.
norm
Specify the norm that each instance must have (default 1.0)
default: 1.0
normalize
Normalize document length (use in conjunction with -norm and -lnorm)
num-decimal-places
The number of decimal places for the output of numbers in the model (default 2).
output-debug-info
If set, classifier is run in debug mode and
may output additional info to the console
stemmer
The stemmering algorihtm (classname plus parameters) to use.
default: weka.core.stemmers.NullStemmer
tokenizer
The tokenizing algorihtm (classname plus parameters) to use.
(default: weka.core.tokenizers.WordTokenizer)