one-hot-encoding

I'm interested in creating interaction terms in h2o.glm(). But I do not want to generate all pairwise interactions. For example, in the mtcars dataset...I want to interact 'mpg' with all the other factors such as 'cyl','hp', and 'disp' but I don't want the other factors to interact with each other (so I don't want disp_hp or disp_cyl). How should I best approach this problem using the (interactions = interactions_list) parameter in h2o.glm() ? Thank you According to ?h2o.glm the interactions= parameter takes: A list of predictor column indices to interact. All pairwise combinations will be

I have 3 sets of data (training, validation and testing) and when I run: training_x = pd.get_dummies(training_x, columns=['a', 'b', 'c']) It gives me a certain number of features. But then when I run it across validation data, it gives me a different number and the same for testing. Is there any way to normalize (wrong word, I know) across all data sets so the number of features aligns? dummies should be created before dividing the dataset into train, test or validate suppose i have train and test dataframe as follows import pandas as pd train = pd.DataFrame([1,2,3], columns= ['A']) test= pd

I have a dataframe like below. The column Mfr Number is a categorical data type. I'd like to preform get_dummies or one hot encoding on it, but instead of filling in the new column with a 1 if it's from that row, I want it to fill in the value from the quantity column. All the other new 'dummies' should remain a 0 on that row. Is this possible? Datetime Mfr Number quantity 0 2016-03-15 07:02:00 MWS0460MB 1 1 2016-03-15 07:03:00 TM-120-6X 3 2 2016-03-15 08:33:00 40.50699.0095 5 3 2016-03-15 08:42:00 40.50699.0100 1 4 2016-03-15 08:46:00 CXS-04T098-00-0703R-1025 10 Do it in two steps: dummies =

Actually,My problem is based on the : Is there a faster way to update dataframe column values based on conditions? So,the data should be: import pandas as pd import io t=""" AV4MdG6Ihowv-SKBN_nB DTP,FOOD AV4Mc2vNhowv-SKBN_Rn Cash 1,FOOD AV4MeisikOpWpLdepWy6 DTP,Bar AV4MeRh6howv-SKBOBOn Cash 1,FOOD AV4Mezwchowv-SKBOB_S DTOT,Bar AV4MeB7yhowv-SKBOA5b DTP,Bar """ data_vec=pd.read_csv(io.StringIO(t),sep='\s{2,}',names=['id','source']) data_vec This is the data_vec: id source 0 AV4MdG6Ihowv-SKBN_nB DTP,FOOD 1 AV4Mc2vNhowv-SKBN_Rn Cash 1,FOOD 2 AV4MeisikOpWpLdepWy6 DTP,Bar 3 AV4MeRh6howv-SKBOBOn Cash

I just started learning machine learning, when practicing one of the task, I am getting value error, but I followed the same steps as the instructor does. I am getting value error, please help. dff Country Name 0 AUS Sri 1 USA Vignesh 2 IND Pechi 3 USA Raj First I performed labelencoding, X=dff.values label_encoder=LabelEncoder() X[:,0]=label_encoder.fit_transform(X[:,0]) out: X array([[0, 'Sri'], [2, 'Vignesh'], [1, 'Pechi'], [2, 'Raj']], dtype=object) then performed One hot encoding for the same X onehotencoder=OneHotEncoder( categorical_features=[0]) X=onehotencoder.fit_transform(X).toarray