1.使用底层的方法来替代使用java层的方法 尽量不要使用setImageBitmap或setImageResource或BitmapFactory...
feature_name = ['feature_' + str(col) for col in range(num_feature)] print('开始训练...') gbm = lgb.train(params, lgb_train, num_boost_round=10, valid_sets=lgb_train, # 评估训练集 feature_name=feature_name, categorical_feature=[21]) 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. ...
.feature_name(): 获取每个特征的名字。 返回值:一个字符串的列表,表示每个特征的名字 .free_dataset():释放Booster对象的数据集 .free_network(): 释放Booster对象的Network .get_leaf_output(tree_id, leaf_id): 获取指定叶子的输出 输入: tree_id: 一个整数,表示子学习器的编号 ...
params, train_set, num_boost_round=100, valid_sets=None, valid_names=None, fobj=None, feval=None, init_model=None, feature_name='auto', categorical_feature='auto', early_stopping_rounds=None, evals_result=None, verbose_eval=True, learning_rates=None, keep_training_booster=False, callbacks...
01, 0.5, name='learning_rate', prior='log-uniform'), skopt.space.Integer(1, 30, name='max_depth'), skopt.space.Integer(10, 200, name='num_leaves'), skopt.space.Real(0.1, 1.0, name='feature_fraction', prior='uniform'), skopt.space.Real(0.1, 1.0, name='subsa...
feature_name = ['feature_' + str(col) for col in range(num_feature)] gbm = lgb.train(params, lgb_train, num_boost_round=10, valid_sets=lgb_train, # eval training data feature_name=feature_name, categorical_feature=[21]) 1.
Environment info Operating System : macOS Catalina Version 10.15.2 CPU/GPU model:CPU C++/Python/R version: Python LightGBM version : 2.3.1 Error message I was trying to get the feature name using the below code , then i encountered such ...
feature_importance = pd.DataFrame() # split and train on folds for fold_n, (train_index, valid_index) in enumerate(folds.split(X)): print(f'Fold {fold_n + 1} started at {time.ctime()}') if type(X) == np.ndarray: X_train, X_valid = X[columns][train_index], X[columns][...
(10,200,name='num_leaves'),skopt.space.Real(0.1,1.0,name='feature_fraction',prior='uniform'),skopt.space.Real(0.1,1.0,name='subsample',prior='uniform')]@skopt.utils.use_named_args(SPACE)defobjective(**params):return-1.0*train_evaluate(params)monitor=sk_utils.NeptuneMonitor()results=skopt...
如果字符串列表，解释为特性名称(也需要指定feature_name)，如果' auto '和 默认auto data是pandas DataFrame，则使⽤pandas分类列 callbacks在每次迭代中应⽤的回调函数列表默认None predict参数 参数参数描述可选值 X输⼊特征矩阵n_samples,n_features raw_score是否预测原始分数默认False num_iter_action预测中...