我连续两次使用RF。
首先,我使用max_features='auto'
和整个数据集(109功能)来适应它,以便执行功能选择。
以下是RandomForestClassifier.feature_importances_
,它正确地为每个功能提供了109分:
[0.00118087, 0.01268531, 0.0017589 , 0.01614814, 0.01105567,
0.0146838 , 0.0187875 , 0.0190427 , 0.01429976, 0.01311706,
0.01702717, 0.00901344, 0.01044047, 0.00932331, 0.01211333,
0.01271825, 0.0095337 , 0.00985686, 0.00952823, 0.01165877,
0.00193286, 0.0012602 , 0.00208145, 0.00203459, 0.00229907,
0.00242616, 0.00051358, 0.00071606, 0.00975515, 0.00171034,
0.01134927, 0.00687018, 0.00987706, 0.01507474, 0.01223525,
0.01170495, 0.00928417, 0.01083082, 0.01302036, 0.01002457,
0.00894818, 0.00833564, 0.00930602, 0.01100774, 0.00818604,
0.00675784, 0.00740617, 0.00185461, 0.00119627, 0.00159034,
0.00154336, 0.00478926, 0.00200773, 0.00063574, 0.00065675,
0.01104192, 0.00246746, 0.01663812, 0.01041134, 0.01401842,
0.02038318, 0.0202834 , 0.01290935, 0.01476593, 0.0108275 ,
0.0118773 , 0.01050919, 0.0111477 , 0.00684507, 0.01170021,
0.01291888, 0.00963295, 0.01161876, 0.00756015, 0.00178329,
0.00065709, 0. , 0.00246064, 0.00217982, 0.00305187,
0.00061284, 0.00063431, 0.01963523, 0.00265208, 0.01543552,
0.0176546 , 0.01443356, 0.01834896, 0.01385694, 0.01320648,
0.00966011, 0.0148321 , 0.01574166, 0.0167107 , 0.00791634,
0.01121442, 0.02171706, 0.01855552, 0.0257449 , 0.02925843,
0.01789742, 0. , 0. , 0.00379275, 0.0024365 ,
0.00333905, 0.00238971, 0.00068355, 0.00075399]
然后,我将数据集转换为先前的拟合,这将减少其维度,然后我重新匹配RF。
给定max_features='auto'
和109个专长,我希望总共有~10个功能,调用rf.feats_importance_
,返回更多(62):
[ 0.01261971, 0.02003921, 0.00961297, 0.02505467, 0.02038449,
0.02353745, 0.01893777, 0.01932577, 0.01681398, 0.01464485,
0.01672119, 0.00748981, 0.01109461, 0.01116948, 0.0087081 ,
0.01056344, 0.00971319, 0.01532258, 0.0167348 , 0.01601214,
0.01522208, 0.01625487, 0.01653784, 0.01483562, 0.01602748,
0.01522369, 0.01581573, 0.01406688, 0.01269036, 0.00884105,
0.02538574, 0.00637611, 0.01928382, 0.02061512, 0.02566056,
0.02180902, 0.01537295, 0.01796305, 0.01171095, 0.01179759,
0.01371328, 0.00811729, 0.01060708, 0.015717 , 0.01067911,
0.01773623, 0.0169396 , 0.0226369 , 0.01547827, 0.01499467,
0.01356075, 0.01040735, 0.01360752, 0.01754145, 0.01446933,
0.01845195, 0.0190799 , 0.02608652, 0.02095663, 0.02939744,
0.01870901, 0.02512201]
为什么呢?它不应该只返回~10个特征重要性吗?
答案 0 :(得分:4)
你误解了max_features
的含义,即
寻找最佳分割时需要考虑的功能数量
转换数据时不是功能的数量。
transform
方法中的threshold
确定了最重要的功能。
阈值:字符串,浮点或无,可选(默认=无)
用于特征选择的阈值。保留重要性大于或等于的特征,而丢弃其他特征。如果“中位数”(分别为“均值”),则阈值是要素重要性的中位数(或平均值)。还可以使用缩放因子(例如,“1.25 * mean”)。如果为None且可用,则使用对象属性阈值。否则,默认使用“mean”。