cat_features = [1, 3, 5]指定分类特征所在的列索引 train_pool = Pool(data=data, cat_features=cat_features) 在上述代码中,我们首先导入了必要的库,然后使用`read_csv`函数从CSV文件中加载数据集。接下来,我们指定了包含分类特征的列索引,并将其存储在一个列表中。最后,我们使用`Pool`类创建了一个CatBoo...
You can try to explicitly pass the cat_features to the bentoml runner. It would be something like this: from catboost import Pool runner = bentoml.catboost.get("bentoml_catboost_model:latest").to_runner() cat_features = [2] # specify your cat_features indexes prediction = runner.predict...
# 需要导入模块: from catboost import CatBoostClassifier [as 别名]# 或者: from catboost.CatBoostClassifier importfit[as 别名]deftrain_catboost_model(df, target, cat_features, params, verbose=True):ifnotisinstance(df, DataFrame):raiseException('DataFrame object expected, but got '+ repr(df))print...
示例2: test_cv_cat_df ▲点赞 6▼ # 需要导入模块: import catboost [as 别名]# 或者: from catboost importCatBoostClassifier[as 别名]deftest_cv_cat_df():X, y = make_classification_df(n_samples=1024, n_num_features=20, n_cat_features=1, class_sep=0.98, random_state=0) X, _ = au...
(X, y, cat_features, None, sample_weight, None, None, None, baseline, use_best_model, eval_set, verbose, logging_level, plot, column_description, verbose_eval) File "/usr/local/lib/python2.7/dist-packages/catboost/core.py", line 780, in _fit self._train(train_pool, eval_set, ...
pred2 = model.predict(Pool(map_cat_features(test_pool.get_features(), train_pool.get_cat_feature_indices()), cat_features=train_pool.get_cat_feature_indices()))assert_check_data(pred1, pred2) 开发者ID:iamnik13,项目名称:catboost,代码行数:10,代码来源:test.py ...
model.fit(df, target, cat_features=cat_features_index, verbose=verbose)returnmodel 开发者ID:bamx23,项目名称:ClickHouse,代码行数:12,代码来源:train.py 注:本文中的catboost.CatBoostClassifier类示例由纯净天空整理自Github/MSDocs等开源代码及文档管理平台,相关代码片段筛选自各路编程大神贡献的开源项目,源码版...
data = map_cat_features(pool.get_features(), pool.get_cat_feature_indices()) model.fit(data, pool.get_label(), pool.get_cat_feature_indices(), sample_weight=np.arange(1, pool.num_row()+1), baseline=baseline, use_best_model=True, eval_set=eval_pool) ...
cboost.fit(X_train, y_train,cat_features=categorical_features_indices,eval_set=(X_validation, y_validation),plot=True)#calculating the class wise prediction probability of cboost modelpred_prob=cboost.predict_proba(test)returnpred_prob