Ignore a column while building a model with SKLearn

Question

With R, one can ignore a variable (column) while building a model with the following syntax:

model = lm(dependant.variable ~ . - ignored.variable, data=my.training,set)

It's very handy when your data set contains indexes or ID.

How would you do that with SKlearn in python, assuming your data are Pandas data frames ?

Answer 1

So this is from my own code I used to do some prediction on StackOverflow last year:

from __future__ import division
from pandas import *
from sklearn import cross_validation
from sklearn import metrics
from sklearn.ensemble import GradientBoostingClassifier

basic_feature_names = [ 'BodyLength'
                      , 'NumTags'
                      , 'OwnerUndeletedAnswerCountAtPostTime'
                      , 'ReputationAtPostCreation'
                      , 'TitleLength'
                      , 'UserAge' ]

fea = # extract the features - removed for brevity
# construct our classifier
clf = GradientBoostingClassifier(n_estimators=num_estimators, random_state=0)
# now fit
clf.fit(fea[basic_feature_names], orig_data['OpenStatusMod'].values)
# now 
priv_fea = # this was my test dataset
# now calculate the predicted classes
pred = clf.predict(priv_fea[basic_feature_names])

So if we wanted a subset of the features for classification I could have done this:

# want to train using fewer features so remove 'BodyLength'
basic_feature_names.remove('BodyLength')

clf.fit(fea[basic_feature_names], orig_data['OpenStatusMod'].values)

So the idea here is that a list can be used to select a subset of the columns in the pandas dataframe, as such we can construct a new list or remove a value and use this for selection

I'm not sure how you could do this easily using numpy arrays as indexing is done differently.