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[英]Select only classes with best metric (f1 score) in a multiclass classification problem
[英]Python Catboost: Multiclass F1 score custom metric
您如何找到多類 Catboost 分類器的每個 class 的 F1 分數? 我已經閱讀了文檔和github 存儲庫,其中有人提出了同樣的問題。 但是,我無法弄清楚實現這一目標的代碼鍛造。 我知道我必須在CatBoostClassifier()
中使用custom_metric
參數,但是當我想要我的多類數據集的每個 class 的F1
分數時,我不知道 custom_metric 可以接受什么custom_metric
。
假設您有一個玩具數據集(來自文檔):
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,
# "eval_metric": "F1", # this doesn't work
"verbose": False}
model = CatBoostClassifier(**params)
model.fit(train_pool, eval_set=validate_pool)
你應該使用 TotalF1
params = {
'leaf_estimation_method': 'Gradient',
'learning_rate': 0.01,
'max_depth': 8,
'bootstrap_type': 'Bernoulli',
'objective': 'MultiClass',
'eval_metric': 'MultiClass',
'subsample': 0.8,
'random_state': 42,
'verbose': 0,
"eval_metric" : 'TotalF1',
"early_stopping_rounds" : 100
}
https://catboost.ai/docs/concepts/loss-functions-multiclassification.html
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