Machine learning 基于统计模型的特征选择
问题陈述: 我正在解决一个问题,我必须预测客户是否会选择贷款。我已将所有可用的数据类型(object,int)转换为整数,现在我的数据如下所示 突出显示的列是我的目标列,其中 0表示是 1表示No 此数据集中有47个独立的列 我想针对我的目标列对这些列进行功能选择 我从Z-test开始Machine learning 基于统计模型的特征选择,machine-learning,statistics,feature-extraction,feature-selection,Machine Learning,Statistics,Feature Extraction,Feature Selection,问题陈述: 我正在解决一个问题,我必须预测客户是否会选择贷款。我已将所有可用的数据类型(object,int)转换为整数,现在我的数据如下所示 突出显示的列是我的目标列,其中 0表示是 1表示No 此数据集中有47个独立的列 我想针对我的目标列对这些列进行功能选择 我从Z-test开始 import numpy as np import scipy.stats as st import scipy.special as sp def feature_selection_pvalue(df,
import numpy as np
import scipy.stats as st
import scipy.special as sp
def feature_selection_pvalue(df,col_name,samp_size=1000):
relation_columns=[]
no_relation_columns=[]
H0='There is no relation between target column and independent column'
H1='There is a relation between target column and independent column'
sample_data[col_name]=df[col_name].sample(samp_size)
samp_mean=sample_data[col_name].mean()
pop_mean=df[col_name].mean()
pop_std=df[col_name].std()
print (pop_mean)
print (pop_std)
print (samp_mean)
n=samp_size
q=.5
#lets calculate z
#z = (samp_mean - pop_mean) / np.sqrt(pop_std*pop_std/n)
z = (samp_mean - pop_mean) / np.sqrt(pop_std*pop_std / n)
print (z)
pval = 2 * (1 - st.norm.cdf(z))
print ('p values is==='+str(pval))
if pval< .05 :
print ('Null hypothesis is Accepted for col ---- >'+H0+col_name)
no_relation_columns.append(col_name)
else:
print ('Alternate Hypothesis is accepted -->'+H1)
relation_columns.append(col_name)
print ('length of list ==='+str(len(relation_columns)))
return relation_columns,no_relation_columns
我的问题是
for items in df.columns:
relation,no_relation=feature_selection_pvalue(df,items,5000)
from sklearn.feature_selection import VarianceThreshold
X = df[['age', 'balance',...]] #select your columns
sel = VarianceThreshold(threshold=(.8 * (1 - .8)))
X_red = sel.fit_transform(X)