zoukankan      html  css  js  c++  java
  • LightBGM之train

      用于模型的训练

    1.说明:

    lightgbm.train(paramstrain_setnum_boost_round=100valid_sets=Nonevalid_names=Nonefobj=Nonefeval=Noneinit_model=Nonefeature_name='auto'categorical_feature='auto'early_stopping_rounds=Noneevals_result=Noneverbose_eval=Truelearning_rates=Nonekeep_training_booster=Falsecallbacks=None)

    Parameters:
    • params (dict) – Parameters for training.
    • train_set (Dataset) – Data to be trained.
    • num_boost_round (intoptional (default=100)) – Number of boosting iterations.
    • valid_sets (list of Datasets or Noneoptional (default=None)) – List of data to be evaluated during training.
    • valid_names (list of string or Noneoptional (default=None)) – Names of valid_sets.
    • fobj (callable or Noneoptional (default=None)) – Customized objective function.
    • feval (callable or Noneoptional (default=None)) – Customized evaluation function. Should accept two parameters: preds, train_data. For multi-class task, the preds is group by class_id first, then group by row_id. If you want to get i-th row preds in j-th class, the access way is preds[j * num_data + i]. Note: should return (eval_name, eval_result, is_higher_better) or list of such tuples. To ignore the default metric corresponding to the used objective, set the metricparameter to the string "None" in params.
    • init_model (stringBooster or Noneoptional (default=None)) – Filename of LightGBM model or Booster instance used for continue training.
    • feature_name (list of strings or 'auto'optional (default="auto")) – Feature names. If ‘auto’ and data is pandas DataFrame, data columns names are used.
    • categorical_feature (list of strings or int, or 'auto'optional (default="auto")) – Categorical features. If list of int, interpreted as indices. If list of strings, interpreted as feature names (need to specify feature_name as well). If ‘auto’ and data is pandas DataFrame, pandas categorical columns are used. All values in categorical features should be less than int32 max value (2147483647). All negative values in categorical features will be treated as missing values.
    • early_stopping_rounds (int or Noneoptional (default=None)) – Activates early stopping. The model will train until the validation score stops improving. Validation score needs to improve at least every early_stopping_rounds round(s) to continue training. Requires at least one validation data and one metric. If there’s more than one, will check all of them. But the training data is ignored anyway. If early stopping occurs, the model will add best_iteration field.
    • evals_result (dict or Noneoptional (default=None)) –

      This dictionary used to store all evaluation results of all the items in valid_sets.

      Example

      With a valid_sets = [valid_set, train_set], valid_names = [‘eval’, ‘train’] and a params = (‘metric’:’logloss’) returns: {‘train’: {‘logloss’: [‘0.48253’, ‘0.35953’, …]}, ‘eval’: {‘logloss’: [‘0.480385’, ‘0.357756’, …]}}.

    • verbose_eval (bool or intoptional (default=True)) –

      Requires at least one validation data. If True, the eval metric on the valid set is printed at each boosting stage. If int, the eval metric on the valid set is printed at every verbose_eval boosting stage. The last boosting stage or the boosting stage found by using early_stopping_rounds is also printed.

      Example

      With verbose_eval = 4 and at least one item in evals, an evaluation metric is printed every 4 (instead of 1) boosting stages.

    • learning_rates (listcallable or Noneoptional (default=None)) – List of learning rates for each boosting round or a customized function that calculates learning_rate in terms of current number of round (e.g. yields learning rate decay).
    • keep_training_booster (booloptional (default=False)) – Whether the returned Booster will be used to keep training. If False, the returned value will be converted into _InnerPredictor before returning. You can still use _InnerPredictor as init_model for future continue training.
    • callbacks (list of callables or Noneoptional (default=None)) – List of callback functions that are applied at each iteration. See Callbacks in Python API for more information.
    Returns:

    booster – The trained Booster model.

    Return type:

    Booster

    2.操作:

      用于训练你的模型,返回的是一个训练好的Booster模型

  • 相关阅读:
    sql子游标不共享造成的硬解析
    oracle 11g em配置
    论文阅读 Characterization of Multiple 3D LiDARs for Localization and Mapping using Normal Distributions Transform
    获取操作系统设备信息CPU/内存/系统类型
    c#收取获取outlook中的邮件内容
    WebBrowser中注入js后ajax报错No Transport 或message":"拒绝访问。 ","description":"拒绝访问
    30行js搞定js弹div,支持右下弹出div层,左下弹出div层,右上弹出div层,左上弹出div层,定时关闭
    vscode把本地已存在的项目提交到github
    websocket测试工具的环境搭建与使用
    C语言:大小写字母串相互转换
  • 原文地址:https://www.cnblogs.com/demo-deng/p/9613279.html
Copyright © 2011-2022 走看看