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I think the real reason is that no matter what crazy machine learning idea you want to implement, you basically just import the data and pass it to a function.
by c3534l 11y ago
I think the real reason is that no matter what crazy machine learning idea you want to implement, you basically just import the data and pass it to a function. Bam. You just trained a convolutional neural net. Calculate the hamming distance? Sure. No idea what that is, but I'll throw it into a random forest with a couple other idea when I'm done, then plot the most important variables. You never really have to learn a new API, which I'm always doing in Python. I don't want to learn a whole new framework: I heard of a thing and I want to see what it does with my data. Python never lets things be as simple as:
from machinelearning import svm
with open('/home/me/programming/data.csv') as f:
data = f.read()
print(svm(data))
That's why R, which is an awful, buggy, and weird language, is so pleasant to use for stats and ML.
- bowyakka 11y agoNot to poke holes in this but is this actually that hard ? import pandas as pd from sklearn.linear import SVC df = pd.read_csv('/home/me/programming/data.csv') y = df['label'] X = df.drop('label', axis=1) clf = SVC() clf.fit(X, y) ... Most of the things have a similar API, except for when I veer off into say deep-learning land. This is not to say that R is a bad language, or that R does not have equally nice API's for this stuff; but I feel that in your case its a familiarity of language thing.
- Lofkin 11y agoYou clearly haven't used pythons major data science packages. Scikitlearn and statsmodels are known as being specifically easy to use with a uniform API that disparate R packages lack(though this is somewhat fixed with caret). Further, for bayesian inference, Pymc 3 is more powerful and concise than training a model in stan.