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Calculated Shard: 169 (from laksa095)

2. Crawled Status Check

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curl -X POST \
  'http://laksa169.int.ahrefs:8124/' \
  -H 'Content-Type: text/plain' \
  -H 'X-ClickHouse-Database: crawler3' \
  -H 'Authorization: Basic YXBpOg==' \
  -d 'SELECT getAhrefsURLFromUnparsed(src_unparsed) AS found_url, ifNull(toUnixTimestamp(download_stamp), 0) AS crawl_time, ifNull(toUnixTimestamp(props_url_first_seen), 0) AS first_indexed_time, download_http_code AS http_code, src_unparsed AS src_unparsed, src_root_hash AS src_root_hash, history_drop_reason AS history_drop_reason, meta_title AS meta_title, meta_descriptions AS meta_descriptions, attrs_boilerpipe_text AS attrs_boilerpipe_text, attrs_markdown AS attrs_markdown, attrs_readable_markdown AS attrs_readable_markdown, meta_canonical AS meta_canonical FROM crawler3.page_info_local FINAL PREWHERE (src_root_hash, src_unparsed) IN ((getAhrefsRootHashFromUnparsed(getAhrefsUnparsedNoserviceFromURL(\'https://stackoverflow.com/questions/61354410/python-catboost-multiclass-f1-score-custom-metric\')), getAhrefsUnparsedNoserviceFromURL(\'https://stackoverflow.com/questions/61354410/python-catboost-multiclass-f1-score-custom-metric\'))) FORMAT JSONEachRow'

Response:

{"found_url":"https:\/\/stackoverflow.com\/questions\/61354410\/python-catboost-multiclass-f1-score-custom-metric","crawl_time":1753503386,"first_indexed_time":1592543967,"http_code":200,"src_unparsed":"com,stackoverflow!\/questions\/61354410\/python-catboost-multiclass-f1-score-custom-metric s443","src_root_hash":"714406497480128969","history_drop_reason":null,"meta_title":"Python Catboost: Multiclass F1 score custom metric - Stack Overflow","meta_descriptions":[],"attrs_boilerpipe_text":"How do you find the F1-score for each class of a multiclass Catboost Classifier? I've already read through the  documentation  and the  github repo  where someone asks the same question. However, I am unable to figure out the codesmithing to achieve this. I understand that I must use the  custom_metric  parameter in  CatBoostClassifier()  but I don't know what arguments are acceptable for  custom_metric  when I want  F1  score for each class of my multiclass dataset. Suppose you have a toy dataset (from the documentation): from  catboost  import  Pool\ncat_features = [ 0 ,  1 ,  2 ]\ndata = [[ \"a\" , \"b\" ,  1 ,  4 ,  5 ,  6 ],\n        [ \"a\" , \"b\" ,  4 ,  5 ,  6 ,  7 ],\n        [ \"c\" , \"d\" ,  30 ,  40 ,  50 ,  60 ]]\n\nlabel = [ 0 ,  1 ,  2 ]\n\n from  sklearn.model_selection  import  train_test_split    \nX_train, X_test, y_train, y_test = train_test_split(data, labels, test_size= 0.2 )\ntrain_pool = Pool(X_train, y_train, cat_features=categorical_features_indices)\nvalidate_pool = Pool(X_test, y_test, cat_features=categorical_features_indices)\nparams = { \"loss_function\" :  \"MultiClass\" ,\n           \"depth\" : symmetric_tree_depth,\n           \"num_trees\" :  500 ,\n \"verbose\" :  False }\n\nmodel = CatBoostClassifier(**params)\nmodel.fit(train_pool, eval_set=validate_pool)\n","attrs_markdown":null,"attrs_readable_markdown":null,"meta_canonical":null}

Filter	Status	Condition	Details
HTTP status	PASS	`download_http_code = 200`	HTTP 200
Age cutoff	FAIL	`download_stamp > now() - 6 MONTH`	8.5 months ago
History drop	PASS	`isNull(history_drop_reason)`	No drop reason
Spam/ban	PASS	`fh_dont_index != 1 AND ml_spam_score = 0`	ml_spam_score=0
Canonical	PASS	`meta_canonical IS NULL OR = '' OR = src_unparsed`	Not set

Property	Value
URL	https://stackoverflow.com/questions/61354410/python-catboost-multiclass-f1-score-custom-metric
Last Crawled	2025-07-26 04:16:26 (8 months ago)
First Indexed	2020-06-19 05:19:27 (5 years ago)
HTTP Status Code	200
Meta Title	Python Catboost: Multiclass F1 score custom metric - Stack Overflow
Meta Description	null
Meta Canonical	null
Boilerpipe Text	How do you find the F1-score for each class of a multiclass Catboost Classifier? I've already read through the documentation and the github repo where someone asks the same question. However, I am unable to figure out the codesmithing to achieve this. I understand that I must use the custom_metric parameter in CatBoostClassifier() but I don't know what arguments are acceptable for custom_metric when I want F1 score for each class of my multiclass dataset. Suppose you have a toy dataset (from the documentation): from catboost import Pool cat_features = [ 0 , 1 , 2 ] data = [[ "a" , "b" , 1 , 4 , 5 , 6 ], [ "a" , "b" , 4 , 5 , 6 , 7 ], [ "c" , "d" , 30 , 40 , 50 , 60 ]] label = [ 0 , 1 , 2 ] from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test = train_test_split(data, labels, test_size= 0.2 ) train_pool = Pool(X_train, y_train, cat_features=categorical_features_indices) validate_pool = Pool(X_test, y_test, cat_features=categorical_features_indices) params = { "loss_function" : "MultiClass" , "depth" : symmetric_tree_depth, "num_trees" : 500 , "verbose" : False } model = CatBoostClassifier(**params) model.fit(train_pool, eval_set=validate_pool)
Markdown	null
Readable Markdown	null
Shard	169 (laksa)
Root Hash	714406497480128969
Unparsed URL	com,stackoverflow!/questions/61354410/python-catboost-multiclass-f1-score-custom-metric s443

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