逻辑回归 SKLEARN 无法将字符串转换为浮点数："每日回报"

Question

尝试运行逻辑回归，但在检查数据DailyReturns列名could not convert string to float: 'DailyReturns'出现此错误。

另外：apple['DailyReturns'].dtype给出dtype('float64')，apple['_lortrain'].dtype给出dtype('float64')

错误：

ValueError: could not convert string to float: 'DailyReturns'

法典：

_lortrain = np.sign(apple['DailyReturns'])
apple['_lortrain'] = _lortrain
_data_train, _data_test,  = train_test_split(apple,test_size = 0.2)
X_train = _data_train.columns[4:9].values.reshape(-1,1)
y_train = _data_train['_lortrain'].values.reshape(-1,1)
#X_train = X_train.apply(pd.to_numeric, errors='coerce')
#y_train = y_train.apply(pd.to_numeric, errors='coerce')
X_test = _data_test.columns[4:9].values.reshape(-1,1)
y_test = _data_test['_lortrain'].values.reshape(-1,1)
_LogR = lor(C = 1e6, penalty='l2', tol=0.01, solver='saga')
_LogR.fit(X_train,y_train)
_logr = _LogR.predict(X_test)

数据：

apple.columns[4:9] ：

Index(['DailyReturns', 'Lag_Returns_1', 'Lag_Returns_2', 'Lag_Returns_3',
       'Lag_Returns_4'],
      dtype='object')
Close   DailyReturns    Lag_Returns_1   Lag_Returns_2   Lag_Returns_3   Lag_Returns_4   Lag_Returns_5
Date                            
1980-12-22  0.53    0.058269    0.040822    0.042560    0.021979    -0.085158   -0.040005
1980-12-23  0.55    0.037041    0.058269    0.040822    0.042560    0.021979    -0.085158
1980-12-24  0.58    0.053110    0.037041    0.058269    0.040822    0.042560    0.021979
1980-12-26  0.63    0.082692    0.053110    0.037041    0.058269    0.040822    0.042560
1980-12-29  0.64    0.015748    0.082692    0.053110    0.037041    0.058269    0.040822

Answer 1

_data_train.columns[4:9].values是列名(字符串(的列表，而不是列数据。您应该使用 _data_train.iloc[:,4:9].values .或者，更好的是：

X_train = _data_train.iloc[:,4:9] # No values or reshape