# Is Random Forest the best way for calculating continuous variable using 5 categorical variable

I have 5 categorical variables. I binarize them into buckets and then assign 1 for the matched and 0 for others.

Next, I run a RandomForestRegressor using scikit-learn. But, my accuracy is only 18% on the test dataset. I don't know what to improve upon ? Should we use Random Forest where the predictor variables are categorical in nature?

categorical = [u'vendor_id', u'part_id', u'ship_to_location_id',
u'bill_to_location_id', u'carrier_number']

df_x.iloc[0].tolist
Out[652]:
<bound method Series.tolist of
vendor_id_435835                           1
vendor_id_437307                           0
vendor_id_422290                           0
vendor_id_421933                           0
vendor_id_425392                           0
vendor_id_421725                           0
vendor_id_421961                           0
vendor_id_437323                           0


The output variable is integer. It is the lag time for each vendor, part_id, source country,destination country and carrier chosen (air, ship, road etc). There is a relationship for sure, but how to effectively predict the delivery lag days which is my output variable. Sample values are as below:

df_y
Out[655]:
0         4
1         1
2         1
3         9
4         1
5        58
6         3
7         7
8         5
9         5
10        7


To check the values, wrote a block of code. As pointed out, accuracy_metrics is only capturing the exact matches.

count = 0
for i,val in enumerate(pred):
if ((val + val * 0.1) == actual[i]):
count += 1
elif ((val - val * 0.1) == actual[i]):
count += 1
elif val == actual[i]:
count += 1
else:
print ("actual:",actual[i],"predicted:",val)


EDIT 2: @Matthew Drury pointed out the MSE would be a better score to track.

import sklearn.metrics as sm
print ("MSE RandomForest:",sm.mean_squared_error(actual,pred))


Using this the RandomForest gave a very small score for MSE.

• What do you mean by "binarize into buckets"? Please describe the raw data (both the predictors and response) in terms of their distributions. It might be a good idea to plot PDFs or PMFs of each variable (including the response) and add them to your question. – Josh May 7 '17 at 17:36