How to use Recursive Feature elimination? - machine-learning

Question

I am new to ML and have been trying Feature selection with RFE approach. My dataset has 5K records and its binary classification problem. This is the code that I am following based on a tutorial online
#no of features
nof_list=np.arange(1,13)
high_score=0
#Variable to store the optimum features
nof=0
score_list =[]
for n in range(len(nof_list)):
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state = 0)
model = RandomForestClassifier()
rfe = RFE(model,nof_list[n])
X_train_rfe = rfe.fit_transform(X_train,y_train)
X_test_rfe = rfe.transform(X_test)
model.fit(X_train_rfe,y_train)
score = model.score(X_test_rfe,y_test)
score_list.append(score)
if(score>high_score):
high_score = score
nof = nof_list[n]
print("Optimum number of features: %d" %nof)
print("Score with %d features: %f" % (nof, high_score))
I encounter the below error. Can someone please help
TypeError Traceback (most recent call last)
<ipython-input-332-a23dfb331001> in <module>
9 model = RandomForestClassifier()
10 rfe = RFE(model,nof_list[n])
---> 11 X_train_rfe = rfe.fit_transform(X_train,y_train)
12 X_test_rfe = rfe.transform(X_test)
13 model.fit(X_train_rfe,y_train)
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\base.py in fit_transform(self, X, y, **fit_params)
554 Training set.
555
--> 556 y : numpy array of shape [n_samples]
557 Target values.
558
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\feature_selection\_base.py in transform(self, X)
75 X = check_array(X, dtype=None, accept_sparse='csr',
76 force_all_finite=not tags.get('allow_nan', True))
---> 77 mask = self.get_support()
78 if not mask.any():
79 warn("No features were selected: either the data is"
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\feature_selection\_base.py in get_support(self, indices)
44 values are indices into the input feature vector.
45 """
---> 46 mask = self._get_support_mask()
47 return mask if not indices else np.where(mask)[0]
48
~\AppData\Local\Continuum\anaconda3\lib\site-packages\sklearn\feature_selection\_rfe.py in _get_support_mask(self)
269
270 def _get_support_mask(self):
--> 271 check_is_fitted(self)
272 return self.support_
273
TypeError: check_is_fitted() missing 1 required positional argument: 'attributes'

Answer 1

What is your sklearn version ?
The following (using artificial data) should work fine:
from sklearn.model_selection import train_test_split
import numpy as np
from sklearn.feature_selection import RFE
from sklearn.ensemble import RandomForestClassifier
X = np.random.rand(100,20)
y = np.ones((X.shape[0]))
#no of features
nof_list=np.arange(1,13)
high_score=0
#Variable to store the optimum features
nof=0
score_list =[]
for n in range(len(nof_list)):
X_train, X_test, y_train, y_test = train_test_split(X,y, test_size = 0.3, random_state = 0)
model = RandomForestClassifier()
rfe = RFE(model,nof_list[n])
X_train_rfe = rfe.fit_transform(X_train,y_train)
X_test_rfe = rfe.transform(X_test)
model.fit(X_train_rfe,y_train)
score = model.score(X_test_rfe,y_test)
score_list.append(score)
if(score>high_score):
high_score = score
nof = nof_list[n]
print("Optimum number of features: %d" %nof)
print("Score with %d features: %f" % (nof, high_score))
Optimum number of features: 1
Score with 1 features: 1.000000
Versions tested:
sklearn.__version__
'0.20.4'
sklearn.__version__
'0.21.3'