對于機器學習訓練來說，每一種特征對于機器學習訓練的模型的重要性可能是不一樣的，此時，這個函數就起到了作用。

下面是文檔

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feature_importance(importance_type='split', iteration=None)

• Get feature importances.
  Parameters:

  importance_type (string, optional (default=“split”))– How the importance is calculated. If “split”, result contains numbers of times the feature is used in a model. If “gain”, result contains total gains of splits which use the feature.

  iteration (int or None, optional (default=None)) – Limit number of iterations in the feature importance calculation. If None, if the best iteration exists, it is used; otherwise, all trees are used. If <= 0, all trees are used (no limits).

下面是一個py文件中的代碼樣例

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for i, (train_ind, val_ind) in enumerate(kf.split(train)): # i 返回的是第幾折， train_ind 是訓練集， val_ind 是測試集print(f'Beginning fold {i}')train_data = lgb.Dataset(X.iloc[train_ind],label=y.iloc[train_ind])val_data = lgb.Dataset(X.iloc[val_ind],label=y.iloc[val_ind])model = lgb.train(params,train_data,num_boost_round=10000,valid_sets = [train_data, val_data],verbose_eval=100,early_stopping_rounds = 100)#Save both split and gain importances, averaged over 5 folds# 統計某種特征在該機器學習算法模型中使用過的次數(這里求的是5次訓練的平均值)feats['importance_split'] += model.feature_importance()/5# 統計某種特征在整個.py文件中使用的次數(這里求的是5次訓練的平均值)feats['importance_gain'] += model.feature_importance(importance_type='gain')/5

總結

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