So, sklearn doesn't support categorical data in its models. Is there a known alternative for categorical data modeling (such as random forests, etc.) for Python?
$\begingroup$
$\endgroup$
-
$\begingroup$ Why not processing / encoding your categorical features ? $\endgroup$ – Theudbald Jan 7 '18 at 15:25
-
$\begingroup$ Are you suggesting one-hot? It seems like a too much of a workaround just because the library doesn't support it $\endgroup$ – shakedzy Jan 7 '18 at 15:53
-
$\begingroup$ Sklearn provides features processing tools including one Hot encoding. See scikit-learn.org/stable/modules/… $\endgroup$ – Theudbald Jan 7 '18 at 16:03
-
$\begingroup$ Still, seems like a workaround simply because this specific library doesn't support it. Is there nothing else? $\endgroup$ – shakedzy Jan 7 '18 at 17:19
$\begingroup$
$\endgroup$
There are definitely ways to process your data to make categorical data compatible with sklearn (e.g one-hot encoding). An alternative you can look into is h2o, which supports categorical features natively (although it doesn't offer the breadth of models of sklearn).
$\begingroup$
$\endgroup$
statsmodel supports Fitting models using R-style formulas:
In [5]: df = sm.datasets.get_rdataset("Guerry", "HistData").data
In [6]: df = df[['Lottery', 'Literacy', 'Wealth', 'Region']].dropna()
In [7]: df.head()
Out[7]:
Lottery Literacy Wealth Region
0 41 37 73 E
1 38 51 22 N
2 66 13 61 C
3 80 46 76 E
4 79 69 83 E
You can fit models without processing categorical data
In [11]: res = smf.ols(formula='Lottery ~ Literacy + Wealth + C(Region)', data=df).fit()
In [12]: print(res.params)
Intercept 38.651655
C(Region)[T.E] -15.427785
C(Region)[T.N] -10.016961
C(Region)[T.S] -4.548257
C(Region)[T.W] -10.091276
Literacy -0.185819
Wealth 0.451475
dtype: float64
