1SA-LEARN(1)           User Contributed Perl Documentation          SA-LEARN(1)
2
3
4

NAME

6       sa-learn - train SpamAssassin's Bayesian classifier
7

SYNOPSIS

9       sa-learn [options] [file]...
10
11       sa-learn [options] --dump [ all | data | magic ]
12
13       Options:
14
15        --ham                 Learn the following messages as ham (non-spam)
16        --spam                Learn the following messages as spam
17        --forget              Forget the following messages
18        --use-ignores         Use bayes_ignore_from and bayes_ignore_to
19        --sync                Synchronize the database and the journal if needed
20        --force-expire        Force a database sync and expiry run
21        --dbpath <path>       Allows commandline override (in bayes_path form)
22                              for where to read the Bayes DB from
23        --dump [all|data|magic]  Display the contents of the Bayes database
24                              Takes optional argument for what to display
25         --regexp <re>        For dump only, specifies which tokens to
26                              dump based on a regular expression.
27        -f file, --folders=file  Read list of files/directories from file
28        --dir                 Ignored; historical compatibility
29        --file                Ignored; historical compatibility
30        --mbox                Input sources are in mbox format
31        --mbx                 Input sources are in mbx format
32        --max-size <b>        Skip messages larger than b bytes;
33                              defaults to 500 KB, 0 implies no limit
34        --showdots            Show progress using dots
35        --progress            Show progress using progress bar
36        --no-sync             Skip synchronizing the database and journal
37                              after learning
38        -L, --local           Operate locally, no network accesses. Use
39                              of this is recommended, see documentation.
40        --import              Migrate data from older version/non DB_File
41                              based databases
42        --clear               Wipe out existing database
43        --backup              Backup, to STDOUT, existing database
44        --restore <filename>  Restore a database from filename
45        -u username, --username=username
46                              Override username taken from the runtime
47                              environment, used with SQL
48        -C path, --configpath=path, --config-file=path
49                              Path to standard configuration dir
50        -p prefs, --prefspath=file, --prefs-file=file
51                              Set user preferences file
52        --siteconfigpath=path Path for site configs
53                              (default:  /usr/etc/mail/spamassassin)
54        --cf='config line'    Additional line of configuration
55        -D, --debug [area,...]  Print debugging messages
56        -V, --version         Print version
57        -h, --help            Print usage message
58

DESCRIPTION

60       Given a typical selection of your incoming mail classified as spam or
61       ham (non-spam), this tool will feed each mail to SpamAssassin, allowing
62       it to 'learn' what signs are likely to mean spam, and which are likely
63       to mean ham.
64
65       Simply run this command once for each of your mail folders, and it will
66       ''learn'' from the mail therein.
67
68       Note that csh-style globbing in the mail folder names is supported; in
69       other words, listing a folder name as "*" will scan every folder that
70       matches.  See "Mail::SpamAssassin::ArchiveIterator" for more details.
71
72       If you are using mail boxes in format other than maildir you should use
73       the --mbox or --mbx parameters.
74
75       Files compressed with gzip/bzip2/xz/lz4/lzip/lzo are uncompressed
76       automatically.  See "Mail::SpamAssassin::ArchiveIterator" for more
77       details.
78
79       SpamAssassin remembers which mail messages it has learnt already, and
80       will not re-learn those messages again, unless you use the --forget
81       option. Messages learnt as spam will have SpamAssassin markup removed,
82       on the fly.
83
84       If you make a mistake and scan a mail as ham when it is spam, or vice
85       versa, simply rerun this command with the correct classification, and
86       the mistake will be corrected.  SpamAssassin will automatically
87       'forget' the previous indications.
88
89       Users of "spamd" who wish to perform training remotely, over a network,
90       should investigate the "spamc -L" switch.
91

OPTIONS

93       --ham
94           Learn the input message(s) in the files following the option as
95           ham.  If you have previously learnt any of the messages as spam,
96           SpamAssassin will forget them first, then re-learn them as ham.
97           Alternatively, if you have previously learnt them as ham, it'll
98           skip them this time around.  If the messages have already been
99           filtered through SpamAssassin, the learner will ignore any
100           modifications SpamAssassin may have made.
101
102       --spam
103           Learn the input message(s) in the files following the option as
104           spam.  If you have previously learnt any of the messages as ham,
105           SpamAssassin will forget them first, then re-learn them as spam.
106           Alternatively, if you have previously learnt them as spam, it'll
107           skip them this time around.  If the messages have already been
108           filtered through SpamAssassin, the learner will ignore any
109           modifications SpamAssassin may havemmade.
110
111       --folders=filename, -f filename
112           sa-learn will read in the list of folders from the specified file,
113           one folder per line in the file.  If the folder is prefixed with
114           "ham:type:" or "spam:type:", sa-learn will learn that folder
115           appropriately, otherwise the folders will be assumed to be of the
116           type specified by --ham or --spam.
117
118           "type" above is optional, but is the same as the standard for
119           ArchiveIterator: mbox, mbx, dir, file, or detect (the default if
120           not specified).
121
122       --mbox
123           sa-learn will read in the file(s) containing the emails to be
124           learned, and will process them in mbox format (one or more emails
125           per file).
126
127       --mbx
128           sa-learn will read in the file(s) containing the emails to be
129           learned, and will process them in mbx format (one or more emails
130           per file).
131
132       --use-ignores
133           Don't learn the message if a from address matches configuration
134           file item "bayes_ignore_from" or a to address matches
135           "bayes_ignore_to".  The option might be used when learning from a
136           large file of messages from which the hammy spam messages or spammy
137           ham messages have not been removed.
138
139       --sync
140           Synchronize the journal and databases.  Upon successfully syncing
141           the database with the entries in the journal, the journal file is
142           removed.
143
144       --force-expire
145           Forces an expiry attempt, regardless of whether it may be necessary
146           or not.  Note: This doesn't mean any tokens will actually expire.
147           Please see the EXPIRATION section below.
148
149           Note: "--force-expire" also causes the journal data to be
150           synchronized into the Bayes databases.
151
152       --forget
153           Forget the input message(s) in the files following the option as
154           previously learnt.
155
156       --dbpath
157           Allows a commandline override of the bayes_path configuration
158           option.
159
160       --dump option
161           Display the contents of the Bayes database.  Without an option or
162           with the all option, all magic tokens and data tokens will be
163           displayed.  magic will only display magic tokens, and data will
164           only display the data tokens.
165
166           Can also use the --regexp RE option to specify which tokens to
167           display based on a regular expression.
168
169       --clear
170           Clear an existing Bayes database by removing all traces of the
171           database.
172
173           WARNING: This is destructive and should be used with care.
174
175       --backup
176           Performs a dump of the Bayes database in machine/human readable
177           format.
178
179           The dump will include token and seen data.  It is suitable for
180           input back into the --restore command.
181
182       --restore=filename
183           Performs a restore of the Bayes database defined by filename.
184
185           WARNING: This is a destructive operation, previous Bayes data will
186           be wiped out.
187
188       -h, --help
189           Print help message and exit.
190
191       -u username, --username=username
192           If specified this username will override the username taken from
193           the runtime environment.  You can use this option to specify users
194           in a virtual user configuration when using SQL as the Bayes
195           backend.
196
197           NOTE: This option will not change to the given username, it will
198           only attempt to act on behalf of that user.  Because of this you
199           will need to have proper permissions to be able to change files
200           owned by username.  In the case of SQL this generally is not a
201           problem.
202
203       -C path, --configpath=path, --config-file=path
204           Use the specified path for locating the distributed configuration
205           files.  Ignore the default directories (usually
206           "/usr/share/spamassassin" or similar).
207
208       --siteconfigpath=path
209           Use the specified path for locating site-specific configuration
210           files.  Ignore the default directories (usually
211           "/etc/mail/spamassassin" or similar).
212
213       --cf='config line'
214           Add additional lines of configuration directly from the command-
215           line, parsed after the configuration files are read.   Multiple
216           --cf arguments can be used, and each will be considered a separate
217           line of configuration.
218
219       -p prefs, --prefspath=prefs, --prefs-file=prefs
220           Read user score preferences from prefs (usually
221           "$HOME/.spamassassin/user_prefs").
222
223       --progress
224           Prints a progress bar (to STDERR) showing the current progress.  In
225           the case where no valid terminal is found this option will behave
226           very much like the --showdots option.
227
228       -D [area,...], --debug [area,...]
229           Produce debugging output. If no areas are listed, all debugging
230           information is printed. Diagnostic output can also be enabled for
231           each area individually; area is the area of the code to instrument.
232           For example, to produce diagnostic output on bayes, learn, and dns,
233           use:
234
235                   spamassassin -D bayes,learn,dns
236
237           Use an empty string (-D '') to indicate no areas when the next item
238           on the command line is a path, to prevent the path from being
239           parsed as an area.
240
241           For more information about which areas (also known as channels) are
242           available, please see the documentation at:
243
244                   C<https://wiki.apache.org/spamassassin/DebugChannels>
245
246           Higher priority informational messages that are suitable for
247           logging in normal circumstances are available with an area of
248           "info".
249
250       --no-sync
251           Skip the slow synchronization step which normally takes place after
252           changing database entries.  If you plan to learn from many folders
253           in a batch, or to learn many individual messages one-by-one, it is
254           faster to use this switch and run "sa-learn --sync" once all the
255           folders have been scanned.
256
257           Clarification: The state of --no-sync overrides the
258           bayes_learn_to_journal configuration option.  If not specified, sa-
259           learn will learn to the database directly.  If specified, sa-learn
260           will learn to the journal file.
261
262           Note: --sync and --no-sync can be specified on the same
263           commandline, which is slightly confusing.  In this case, the
264           --no-sync option is ignored since there is no learn operation.
265
266       -L, --local
267           Do not perform any network accesses while learning details about
268           the mail messages.  This should be normally used, as there really
269           isn't anything Bayes can learn from network lookup results.
270           Official SpamAssassin plugins do not currently do any network
271           lookups when learning, but it's possible that third party ones
272           might.
273
274       --import
275           If you previously used SpamAssassin's Bayesian learner without the
276           "DB_File" module installed, it will have created files in other
277           formats, such as "GDBM_File", "NDBM_File", or "SDBM_File".  This
278           switch allows you to migrate that old data into the "DB_File"
279           format.  It will overwrite any data currently in the "DB_File".
280
281           Can also be used with the --dbpath path option to specify the
282           location of the Bayes files to use.
283

MIGRATION

285       There are now multiple backend storage modules available for storing
286       user's bayesian data. As such you might want to migrate from one
287       backend to another. Here is a simple procedure for migrating from one
288       backend to another.
289
290       Note that if you have individual user databases you will have to
291       perform a similar procedure for each one of them.
292
293       sa-learn --sync
294           This will sync any outstanding journal entries
295
296       sa-learn --backup > backup.txt
297           This will save all your Bayes data to a plain text file.
298
299       sa-learn --clear
300           This is optional, but good to do to clear out the old database.
301
302       Repeat!
303           At this point, if you have multiple databases, you should perform
304           the procedure above for each of them. (i.e. each user's database
305           needs to be backed up before continuing.)
306
307       Switch backends
308           Once you have backed up all databases you can update your
309           configuration for the new database backend. This will involve at
310           least the bayes_store_module config option and may involve some
311           additional config options depending on what is required by the
312           module. (For example, you may need to configure an SQL database.)
313
314       sa-learn --restore backup.txt
315           Again, you need to do this for every database.
316
317       If you are migrating to SQL you can make use of the -u <username>
318       option in sa-learn to populate each user's database. Otherwise, you
319       must run sa-learn as the user who database you are restoring.
320

INTRODUCTION TO BAYESIAN FILTERING

322       (Thanks to Michael Bell for this section!)
323
324       For a more lengthy description of how this works, go to
325       http://www.paulgraham.com/ and see "A Plan for Spam". It's reasonably
326       readable, even if statistics make me break out in hives.
327
328       The short semi-inaccurate version: Given training, a spam heuristics
329       engine can take the most "spammy" and "hammy" words and apply
330       probabilistic analysis. Furthermore, once given a basis for the
331       analysis, the engine can continue to learn iteratively by applying both
332       the non-Bayesian and Bayesian rulesets together to create evolving
333       "intelligence".
334
335       SpamAssassin 2.50 and later supports Bayesian spam analysis, in the
336       form of the BAYES rules. This is a new feature, quite powerful, and is
337       disabled until enough messages have been learnt.
338
339       The pros of Bayesian spam analysis:
340
341       Can greatly reduce false positives and false negatives.
342           It learns from your mail, so it is tailored to your unique e-mail
343           flow.
344
345       Once it starts learning, it can continue to learn from SpamAssassin and
346       improve over time.
347
348       And the cons:
349
350       A decent number of messages are required before results are useful for
351       ham/spam determination.
352       It's hard to explain why a message is or isn't marked as spam.
353           i.e.: a straightforward rule, that matches, say, "VIAGRA" is easy
354           to understand. If it generates a false positive or false negative,
355           it is fairly easy to understand why.
356
357           With Bayesian analysis, it's all probabilities - "because the past
358           says it is likely as this falls into a probabilistic distribution
359           common to past spam in your systems". Tell that to your users!
360           Tell that to the client when he asks "what can I do to change
361           this". (By the way, the answer in this case is "use
362           welcomelisting".)
363
364       It will take disk space and memory.
365           The databases it maintains take quite a lot of resources to store
366           and use.
367

GETTING STARTED

369       Still interested? Ok, here's the guidelines for getting this working.
370
371       First a high-level overview:
372
373       Build a significant sample of both ham and spam.
374           I suggest several thousand of each, placed in SPAM and HAM
375           directories or mailboxes.  Yes, you MUST hand-sort this - otherwise
376           the results won't be much better than SpamAssassin on its own.
377           Verify the spamminess/haminess of EVERY message.  You're urged to
378           avoid using a publicly available corpus (sample) - this must be
379           taken from YOUR mail server, if it is to be statistically useful.
380           Otherwise, the results may be pretty skewed.
381
382       Use this tool to teach SpamAssassin about these samples, like so:
383                   sa-learn --spam /path/to/spam/folder
384                   sa-learn --ham /path/to/ham/folder
385                   sa-learn --ham hampath1 hampath2 --spam spampath1 spampath2
386                   ...
387
388           Let SpamAssassin proceed, learning stuff. When it finds ham and
389           spam it will add the "interesting tokens" to the database.
390
391       If you need SpamAssassin to forget about specific messages, use the
392       --forget option.
393           This can be applied to either ham or spam that has run through the
394           sa-learn processes. It's a bit of a hammer, really, lowering the
395           weighting of the specific tokens in that message (only if that
396           message has been processed before).
397
398       Learning from single messages uses a command like this:
399                   sa-learn --ham --no-sync mailmessage
400
401           This is handy for binding to a key in your mail user agent.  It's
402           very fast, as all the time-consuming stuff is deferred until you
403           run with the "--sync" option.
404
405       Autolearning is enabled by default
406           If you don't have a corpus of mail saved to learn, you can let
407           SpamAssassin automatically learn the mail that you receive.  If you
408           are autolearning from scratch, the amount of mail you receive will
409           determine how long until the BAYES_* rules are activated.
410

EFFECTIVE TRAINING

412       Learning filters require training to be effective.  If you don't train
413       them, they won't work.  In addition, you need to train them with new
414       messages regularly to keep them up-to-date, or their data will become
415       stale and impact accuracy.
416
417       You need to train with both spam and ham mails.  One type of mail alone
418       will not have any effect.
419
420       Note that if your mail folders contain things like forwarded spam,
421       discussions of spam-catching rules, etc., this will cause trouble.  You
422       should avoid scanning those messages if possible.  (An easy way to do
423       this is to move them aside, into a folder which is not scanned.)
424
425       If the messages you are learning from have already been filtered
426       through SpamAssassin, the learner will compensate for this.  In effect,
427       it learns what each message would look like if you had run
428       "spamassassin -d" over it in advance.
429
430       Another thing to be aware of, is that typically you should aim to train
431       with at least 1000 messages of spam, and 1000 ham messages, if
432       possible.  More is better, but anything over about 5000 messages does
433       not improve accuracy significantly in our tests.
434
435       Be careful that you train from the same source -- for example, if you
436       train on old spam, but new ham mail, then the classifier will think
437       that a mail with an old date stamp is likely to be spam.
438
439       It's also worth noting that training with a very small quantity of ham,
440       will produce atrocious results.  You should aim to train with at least
441       the same amount (or more if possible!) of ham data than spam.
442
443       On an on-going basis, it is best to keep training the filter to make
444       sure it has fresh data to work from.  There are various ways to do
445       this:
446
447       1. Supervised learning
448           This means keeping a copy of all or most of your mail, separated
449           into spam and ham piles, and periodically re-training using those.
450           It produces the best results, but requires more work from you, the
451           user.
452
453           (An easy way to do this, by the way, is to create a new folder for
454           'deleted' messages, and instead of deleting them from other
455           folders, simply move them in there instead.  Then keep all spam in
456           a separate folder and never delete it.  As long as you remember to
457           move misclassified mails into the correct folder set, it is easy
458           enough to keep up to date.)
459
460       2. Unsupervised learning from Bayesian classification
461           Another way to train is to chain the results of the Bayesian
462           classifier back into the training, so it reinforces its own
463           decisions.  This is only safe if you then retrain it based on any
464           errors you discover.
465
466           SpamAssassin does not support this method, due to experimental
467           results which strongly indicate that it does not work well, and
468           since Bayes is only one part of the resulting score presented to
469           the user (while Bayes may have made the wrong decision about a
470           mail, it may have been overridden by another system).
471
472       3. Unsupervised learning from SpamAssassin rules
473           Also called 'auto-learning' in SpamAssassin.  Based on statistical
474           analysis of the SpamAssassin success rates, we can automatically
475           train the Bayesian database with a certain degree of confidence
476           that our training data is accurate.
477
478           It should be supplemented with some supervised training in
479           addition, if possible.
480
481           This is the default, but can be turned off by setting the
482           SpamAssassin configuration parameter "bayes_auto_learn" to 0.
483
484       4. Mistake-based training
485           This means training on a small number of mails, then only training
486           on messages that SpamAssassin classifies incorrectly.  This works,
487           but it takes longer to get it right than a full training session
488           would.
489

FILES

491       sa-learn and the other parts of SpamAssassin's Bayesian learner, use a
492       set of persistent database files to store the learnt tokens, as
493       follows.
494
495       bayes_toks
496           The database of tokens, containing the tokens learnt, their count
497           of occurrences in ham and spam, and the timestamp when the token
498           was last seen in a message.
499
500           This database also contains some 'magic' tokens, as follows: the
501           version number of the database, the number of ham and spam messages
502           learnt, the number of tokens in the database, and timestamps of:
503           the last journal sync, the last expiry run, the last expiry token
504           reduction count, the last expiry timestamp delta, the oldest token
505           timestamp in the database, and the newest token timestamp in the
506           database.
507
508           This is a database file, using "DB_File".  The database 'version
509           number' is 0 for databases from 2.5x, 1 for databases from certain
510           2.6x development releases, 2 for 2.6x, and 3 for 3.0 and later
511           releases.
512
513       bayes_seen
514           A map of Message-Id and some data from headers and body to what
515           that message was learnt as. This is used so that SpamAssassin can
516           avoid re-learning a message it has already seen, and so it can
517           reverse the training if you later decide that message was learnt
518           incorrectly.
519
520           This is a database file, using "DB_File".
521
522       bayes_journal
523           While SpamAssassin is scanning mails, it needs to track which
524           tokens it uses in its calculations.  To avoid the contention of
525           having each SpamAssassin process attempting to gain write access to
526           the Bayes DB, the token timestamps are written to a 'journal' file
527           which will later (either automatically or via "sa-learn --sync") be
528           used to synchronize the Bayes DB.
529
530           Also, through the use of "bayes_learn_to_journal", or when using
531           the "--no-sync" option with sa-learn, the actual learning data will
532           take be placed into the journal for later synchronization.  This is
533           typically useful for high-traffic sites to avoid the same
534           contention as stated above.
535

EXPIRATION

537       Since SpamAssassin can auto-learn messages, the Bayes database files
538       could increase perpetually until they fill your disk.  To control this,
539       SpamAssassin performs journal synchronization and bayes expiration
540       periodically when certain criteria (listed below) are met.
541
542       SpamAssassin can sync the journal and expire the DB tokens either
543       manually or opportunistically.  A journal sync is due if --sync is
544       passed to sa-learn (manual), or if the following is true
545       (opportunistic):
546
547       - bayes_journal_max_size does not equal 0 (means don't sync)
548       - the journal file exists
549
550       and either:
551
552       - the journal file has a size greater than bayes_journal_max_size
553
554       or
555
556       - a journal sync has previously occurred, and at least 1 day has passed
557       since that sync
558
559       Expiry is due if --force-expire is passed to sa-learn (manual), or if
560       all of the following are true (opportunistic):
561
562       - the last expire was attempted at least 12hrs ago
563       - bayes_auto_expire does not equal 0
564       - the number of tokens in the DB is > 100,000
565       - the number of tokens in the DB is > bayes_expiry_max_db_size
566       - there is at least a 12 hr difference between the oldest and newest
567       token atimes
568
569   EXPIRE LOGIC
570       If either the manual or opportunistic method causes an expire run to
571       start, here is the logic that is used:
572
573       - figure out how many tokens to keep.  take the larger of either
574       bayes_expiry_max_db_size * 75% or 100,000 tokens.  therefore, the goal
575       reduction is number of tokens - number of tokens to keep.
576       - if the reduction number is < 1000 tokens, abort (not worth the
577       effort).
578       - if an expire has been done before, guesstimate the new atime delta
579       based on the old atime delta.  (new_atime_delta = old_atime_delta *
580       old_reduction_count / goal)
581       - if no expire has been done before, or the last expire looks "weird",
582       do an estimation pass.  The definition of "weird" is:
583           - last expire over 30 days ago
584           - last atime delta was < 12 hrs
585           - last reduction count was < 1000 tokens
586           - estimated new atime delta is < 12 hrs
587           - the difference between the last reduction count and the goal
588           reduction count is > 50%
589
590   ESTIMATION PASS LOGIC
591       Go through each of the DB's tokens.  Starting at 12hrs, calculate
592       whether or not the token would be expired (based on the difference
593       between the token's atime and the db's newest token atime) and keep the
594       count.  Work out from 12hrs exponentially by powers of 2.  ie: 12hrs *
595       1, 12hrs * 2, 12hrs * 4, 12hrs * 8, and so on, up to 12hrs * 512
596       (6144hrs, or 256 days).
597
598       The larger the delta, the smaller the number of tokens that will be
599       expired.  Conversely, the number of tokens goes up as the delta gets
600       smaller.  So starting at the largest atime delta, figure out which
601       delta will expire the most tokens without going above the goal
602       expiration count.  Use this to choose the atime delta to use, unless
603       one of the following occurs:
604
605       - the largest atime (smallest reduction count) would expire too many
606       tokens.  this means the learned tokens are mostly old and there needs
607       to be new tokens learned before an expire can occur.
608       - all of the atime choices result in 0 tokens being removed. this means
609       the tokens are all newer than 12 hours and there needs to be new tokens
610       learned before an expire can occur.
611       - the number of tokens that would be removed is < 1000.  the benefit
612       isn't worth the effort.  more tokens need to be learned.
613
614       If the expire run gets past this point, it will continue to the end.  A
615       new DB is created since the majority of DB libraries don't shrink the
616       DB file when tokens are removed.  So we do the "create new, migrate old
617       to new, remove old, rename new" shuffle.
618
619   EXPIRY RELATED CONFIGURATION SETTINGS
620       "bayes_auto_expire" is used to specify whether or not SpamAssassin
621       ought to opportunistically attempt to expire the Bayes database. The
622       default is 1 (yes).
623       "bayes_expiry_max_db_size" specifies both the auto-expire token count
624       point, as well as the resulting number of tokens after expiry as
625       described above.  The default value is 150,000, which is roughly
626       equivalent to a 6Mb database file if you're using DB_File.
627       "bayes_journal_max_size" specifies how large the Bayes journal will
628       grow before it is opportunistically synced.  The default value is
629       102400.
630

INSTALLATION

632       The sa-learn command is part of the Mail::SpamAssassin Perl module.
633       Install this as a normal Perl module, using "perl -MCPAN -e shell", or
634       by hand.
635

SEE ALSO

637       spamassassin(1) spamc(1) Mail::SpamAssassin(3)
638       Mail::SpamAssassin::ArchiveIterator(3)
639
640       <http://www.paulgraham.com/> Paul Graham's "A Plan For Spam" paper
641
642       <http://www.linuxjournal.com/article/6467> Gary Robinson's f(x) and
643       combining algorithms, as used in SpamAssassin
644
645       <http://web.archive.org/web/20120512230723/http://www.bgl.nu/~glouis/bogofilter/>
646       'Training on error' page.  A discussion of various Bayes training
647       regimes, including 'train on error' and unsupervised training.
648

PREREQUISITES

650       "Mail::SpamAssassin"
651

AUTHORS

653       The SpamAssassin(tm) Project <https://spamassassin.apache.org/>
654
655
656
657perl v5.36.0                      2023-01-21                       SA-LEARN(1)
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