Eu tenho um DataFrame Pandas com umdate coluna (por exemplo:2013-04-01) do dtypedatetime.date. Quando incluo essa coluna emX_train e tentar encaixar o modelo de regressão, recebo o errofloat() argument must be a string or a number. Removendo odate coluna evitou esse erro.

Qual é a maneira correta de tomar odate em conta no modelo de regressão?

Código

data = sql.read_frame(...)
X_train = data.drop('y', axis=1)
y_train = data.y

rf = RandomForestRegressor().fit(X_train, y_train)

Erro

TypeError                                 Traceback (most recent call last)
<ipython-input-35-8bf6fc450402> in <module>()
----> 2 rf = RandomForestRegressor().fit(X_train, y_train)

C:\Python27\lib\site-packages\sklearn\ensemble\forest.pyc in fit(self, X, y, sample_weight)
    292                 X.ndim != 2 or
    293                 not X.flags.fortran):
--> 294             X = array2d(X, dtype=DTYPE, order="F")
    295 
    296         n_samples, self.n_features_ = X.shape

C:\Python27\lib\site-packages\sklearn\utils\validation.pyc in array2d(X, dtype, order, copy)
     78         raise TypeError('A sparse matrix was passed, but dense data '
     79                         'is required. Use X.toarray() to convert to dense.')
---> 80     X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order)
     81     _assert_all_finite(X_2d)
     82     if X is X_2d and copy:

C:\Python27\lib\site-packages\numpy\core\numeric.pyc in asarray(a, dtype, order)
    318 
    319     """
--> 320     return array(a, dtype, copy=False, order=order)
    321 
    322 def asanyarray(a, dtype=None, order=None):

TypeError: float() argument must be a string or a number