sklearn学习：使用sklearn进行特征选择（未完）

1.lasso(下面的case实验成功，在6w+，163维度上未实验成功，可能由于特征的区分度不足引发）

from sklearn.linear_model import RandomizedLasso
from sklearn.datasets import load_boston
boston = load_boston()

#using the Boston housing data.
#Data gets scaled automatically by sklearn's implementation
X = boston["data"]
Y = boston["target"]
names = boston["feature_names"]

rlasso = RandomizedLasso(alpha=0.025)
rlasso.fit(X, Y)

print "Features sorted by their score:"
print sorted(zip(map(lambda x: round(x, 4), rlasso.scores_),
names), reverse=True)

2.通过模型设置特征

2.1 通过logistic进行筛选:linear_model.LogisticRegression(C=0.4,penalty='l1',solver='liblinear')  C为正则项目，C值约小，特征中为0的项目约多，反之越少