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对于机器学习训练来说,每一种特征对于机器学习训练的模型的重要性可能是不一样的,此时,这个函数就起到了作用。
下面是文档
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).
下面是一个py文件中的代码样例
for i, (train_ind, val_ind) in enumerate(kf.split(train)): # i 返回的是第几折, train_ind 是训练集, val_ind 是测试集
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
# 统计某种特征在该机器学习算法模型中使用过的次数(这里求的是5次训练的平均值)
feats['importance_split'] += model.feature_importance()/5
# 统计某种特征在整个.py文件中使用的次数(这里求的是5次训练的平均值)
feats['importance_gain'] += model.feature_importance(importance_type='gain')/5
本文标签: 函数lgbfeatureimportance
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