pyspark GBTRegressor 特征重要度 及排序
2021-04-07 13:25
标签：com meta mode net end pen 随机森林 lam bsp 和随机森林类似，模型评估指标和特征重要度分析 训练好model 可用如下代码打印特征以及重要度排序 参考链接 https://blog.csdn.net/zx8167107/article/details/101709245?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.channel_param&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.channel_param https://blog.csdn.net/qq_23860475/article/details/90766237 pyspark GBTRegressor 特征重要度 及排序 标签：com meta mode net end pen 随机森林 lam bsp 原文地址：https://www.cnblogs.com/Allen-rg/p/13390083.html#打印特征索引及其重要度
features_important = model.featureImportances
print(features_important)
#获取各个特征在模型中的重要性并按照权重倒序打印
ks = list(features_important.indices)
vs = list(features_important.toArray())
features_important = tuple(features_important)
print(len(features_important))
name_index = train.schema["features"].metadata["ml_attr"]["attrs"]
index_im = zip(ks, vs)
names = []
idxs = []
fea_num = 0
for it in name_index[‘numeric‘]:
names.append(it[‘name‘])
idxs.append(it[‘idx‘])
fea_num += 1
print (fea_num)
d = zip(names, idxs)
p = zip(index_im, d)
kv = {}
for fir, sec in p:
kv[sec[0]] = fir[1]
fea_num += 1
print(len(kv))
print (sorted(kv.items(), key=lambda el: el[1], reverse=True))
文章标题：pyspark GBTRegressor 特征重要度 及排序
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