LightGBM如何将feature_fraction转换为整数值？
How does LightGBM convert feature_fraction to an integer value?
有谁知道lightgbm如何将用户定义为非整数（如0.8）的feature_fraction参数转换为整数值？
是用floor还是ceiling功能？
我在文档中找不到它。 （并浏览 GitHub 上的源代码）
文档说：
feature_fraction , default = 1.0, type = double, ... , constraints: 0.0 < feature_fraction <= 1.0
LightGBM will randomly select a subset of features on each iteration (tree) if feature_fraction is smaller than 1.0. For example, if you set it to 0.8, LightGBM will select 80% of features before training each tree.
如果我有三个特征，feature_fraction = 0.5 是什么意思？每个决策树在每次拆分时使用多少个特征？ 1 还是 2？
我在 microsoft/LightGBM GitHub 上问了这个问题。 LightGBM 将使用此公式进行转换，例如 Python:
total_cnt = 3 # I have three features
# I like my decision trees use 50 % of features at each split
feature_fraction = 0.5
# this is the integer value: number of features at each split
max_features = np.floor((feature_fraction * total_cnt) + 0.5)
更多详情请查看here。
有谁知道lightgbm如何将用户定义为非整数（如0.8）的feature_fraction参数转换为整数值？
是用floor还是ceiling功能？
我在文档中找不到它。 （并浏览 GitHub 上的源代码）
文档说：
feature_fraction, default = 1.0, type = double, ... , constraints: 0.0 < feature_fraction <= 1.0 LightGBM will randomly select a subset of features on each iteration (tree) if feature_fraction is smaller than 1.0. For example, if you set it to 0.8, LightGBM will select 80% of features before training each tree.
如果我有三个特征，feature_fraction = 0.5 是什么意思？每个决策树在每次拆分时使用多少个特征？ 1 还是 2？
我在 microsoft/LightGBM GitHub 上问了这个问题。 LightGBM 将使用此公式进行转换，例如 Python:
total_cnt = 3 # I have three features
# I like my decision trees use 50 % of features at each split
feature_fraction = 0.5
# this is the integer value: number of features at each split
max_features = np.floor((feature_fraction * total_cnt) + 0.5)
更多详情请查看here。