Hi,
> What you get from DictVectorizer is a sparse matrix containing one-hot
> coded categorical values (booleans). Random forests don't support
> those, but fortunately they (should) handle categorical values without
> one-hot coding, so you do something like
>
>
I tried with string values and got no success. I have a csv file and
preprocessing it:
for line in f:
sample = []
line = line.strip().split(",")
for x in line:
try:
sample.append(float(x))
except ValueError:
sample.append(str(x))
samples.append(sample)
#sample = [float(x) for x in line]
#samples.append(sample)
return samples
This is from csv_io.read_data at the above code. When the fit function is
called below is the error i got:
File "/usr/lib/pymodules/python2.7/sklearn/ensemble/forest.py", line 294,
in fit
X = array2d(X, dtype=DTYPE, order="F")
File "/usr/lib/pymodules/python2.7/sklearn/utils/validation.py", line 80,
in array2d
X_2d = np.asarray(np.atleast_2d(X), dtype=dtype, order=order)
File "/usr/lib/python2.7/dist-packages/numpy/core/numeric.py", line 320,
in asarray
return array(a, dtype, copy=False, order=order)
ValueError: could not convert string to float: SF
Either using string values is not a good idea or i am doing something
wrong. Any idea?
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