Python 关于Scikit学习的特征选择回归
我有一个excel数据文件，包括预测值和目标/响应。我将目标称为“接近”，每个预测器都有自己的名称。为了研究特征选择，我使用了这段代码，但出现了一些问题，我无法意识到每个预测因素对目标的影响。我写的代码如下。一切帮助都将不胜感激Python 关于Scikit学习的特征选择回归,python,machine-learning,scikit-learn,neural-network,feature-selection,Python,Machine Learning,Scikit Learn,Neural Network,Feature Selection,我有一个excel数据文件，包括预测值和目标/响应。我将目标称为“接近”，每个预测器都有自己的名称。为了研究特征选择，我使用了这段代码，但出现了一些问题，我无法意识到每个预测因素对目标的影响。我写的代码如下。一切帮助都将不胜感激 #importing libraries from sklearn.datasets import load_boston import pandas as pd import numpy as np import matplotlib import matplotli
#importing libraries
from sklearn.datasets import load_boston
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
import statsmodels.api as sm
%matplotlib inline
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.feature_selection import RFE
from sklearn.linear_model import RidgeCV, LassoCV, Ridge, Lasso
# reading files
x = pd.read_excel("train.xlsx")
X = x.iloc[:, 0:5]
y = x.iloc[:,5]
#Using Pearson Correlation
plt.figure(figsize=(12,10))
cor = X.corr()
sns.heatmap(cor, annot=True, cmap=plt.cm.Reds)
plt.show()
#Correlation with output variable
cor_target = abs(cor["NEAR"])
#Selecting highly correlated features
relevant_features = cor_target[cor_target > 0.5]
relevant_features
---------------------------------------------------------------------------
KeyError Traceback (most recent call last)
<ipython-input-28-3468dfbea7e1> in <module>
1 #Correlation with output variable
----> 2 cor_target = abs(cor[y])
3 #Selecting highly correlated features
4 relevant_features = cor_target[cor_target > 0.5]
5 relevant_features
~\Anaconda3\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
2984 if is_iterator(key):
2985 key = list(key)
-> 2986 indexer = self.loc._convert_to_indexer(key, axis=1, raise_missing=True)
2987
2988 # take() does not accept boolean indexers
~\Anaconda3\lib\site-packages\pandas\core\indexing.py in _convert_to_indexer(self, obj, axis, is_setter, raise_missing)
1283 # When setting, missing keys are not allowed, even with .loc:
1284 kwargs = {"raise_missing": True if is_setter else raise_missing}
-> 1285 return self._get_listlike_indexer(obj, axis, **kwargs)[1]
1286 else:
1287 try:
~\Anaconda3\lib\site-packages\pandas\core\indexing.py in _get_listlike_indexer(self, key, axis, raise_missing)
1090
1091 self._validate_read_indexer(
-> 1092 keyarr, indexer, o._get_axis_number(axis), raise_missing=raise_missing
1093 )
1094 return keyarr, indexer
~\Anaconda3\lib\site-packages\pandas\core\indexing.py in _validate_read_indexer(self, key, indexer, axis, raise_missing)
1175 raise KeyError(
1176 "None of [{key}] are in the [{axis}]".format(
-> 1177 key=key, axis=self.obj._get_axis_name(axis)
1178 )
1179 )
KeyError: "None of [Float64Index([-0.00398046, 0.00205926, -0.00304156, 0.00206342, 0.00797852,\n 0.00619195, 0.00368038, 0.00415858, 0.00454432, 0.00536623,\n ...\n 0.00201033, 0.00184575, 0.00165407, 0.00148248, 0.00131221,\n 0.00103276, 0.00084394, 0.00078347, 0.00069564, 0.00058571],\n dtype='float64', length=209076)] are in the [columns]"
---------------------------------------------------------------------------
KeyError回溯（最近一次呼叫最后一次）
在里面
1#与输出变量的相关性
---->2 cor_目标=绝对值（cor[y]）
3#选择高度相关的特征
4相关特征=相关目标[相关目标>0.5]
5相关的功能
~\Anaconda3\lib\site packages\pandas\core\frame.py in\uuuu\getitem\uuuuuuu（self，key）
2984如果是迭代器（键）：
2985键=列表（键）
->2986索引器=self.loc.\u将\u转换为\u索引器（键，轴=1，上升\u缺失=True）
2987
2988#take（）不接受布尔索引器
~\Anaconda3\lib\site packages\pandas\core\index.py in\u convert\u to\u indexer（self、obj、axis、is\u setter、raise\u缺失）
1283#设置时，即使使用.loc:
1284 kwargs={“raise_missing”：如果SEUTER else raise_missing}
->1285返回自我。获取列表式索引器（obj，轴，**kwargs）[1]
1286其他：
1287尝试：
~\Anaconda3\lib\site packages\pandas\core\index.py in\u get\u listlike\u索引器（self、key、axis、raise\u缺失）
1090
1091自我验证读取索引器(
->1092 keyarr，索引器，o._获取_轴编号（轴），raise_missing=raise_missing
1093 )
1094返回键，索引器
~\Anaconda3\lib\site packages\pandas\core\index.py in\u validate\u read\u indexer（self、key、indexer、axis、raise\u缺失）
1175上升键错误(
1176“所有[{key}]都不是[{axis}]”格式(
->1177键=键，轴=自身。对象。\u获取\u轴\u名称（轴）
1178 )
1179 )
KeyError:“没有[Float64Index]([-0.00398046,0.00205926，-0.00304156,0.00206342,0.00797852,n 0.00619195,0.00368038,0.00415858,0.00454432,0.00536623,n…\n 0.0020033,0.00184575,0.00165407,0.00148248,0.00131221,0.00103276,0.00084394,0.00078347,0.00069564,0.00058571]，\n dtype='float64'，length=209076）]在[列]中。“
cor_target = abs(cor[y])
cor_target = abs(cor["NEAR"])
coryy-0.00398046、0.00205926、-0.00304156附近的