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General Assembly's Data Science course in Washington, DC
Hi!
First of all, thanks for the great tutorial! :)
Next, I was following the notes and when trying to run the code snippet
In[21]: lm.predict([100, 25, 25])
for my own problem I got the following error:
/var/ml/python/local/lib/python2.7/site-packages/sklearn/utils/validation.py:386: DeprecationWarning: Passing 1d arrays as data is deprecated in 0.17 and willraise ValueError in 0.19. Reshape your data either using X.reshape(-1, 1) if your data has a single feature or X.reshape(1, -1) if it contains a single sample.
DeprecationWarning)
Adding additional pair of brackets solved it, meaning
lm.predict([[100, 25, 25]])
I assume the implementation has changed for sklearn.linear_model?
Reference: https://github.com/justmarkham/DAT4/blob/master/notebooks/08_linear_regression.ipynb
seems like the choices are the same!
http://nbviewer.ipython.org/github/justmarkham/DAT4/blob/master/notebooks/08_linear_regression.ipynb
Confidence in our Model
Question: Is linear regression a high bias/low variance model, or a low variance/high bias model?
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