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- Difference between scikit-learn and sklearn (now deprecated)
Regarding the difference sklearn vs scikit-learn: The package "scikit-learn" is recommended to be installed using pip install scikit-learn but in your code imported using import sklearn A bit confusing, because you can also do pip install sklearn and will end up with the same scikit-learn package installed, because there is a "dummy" pypi package sklearn which will install scikit-learn for
- How is the R2 value in Scikit learn calculated? - Stack Overflow
The R^2 in scikit learn is essentially the same as what is described in the wikipedia article on the coefficient of determination (grep for "the most general definition") It is 1 - residual sum of square total sum of squares The big difference between a classical stats setting and what you usually try to do with machine learning, is that in machine learning you evaluate your score on
- python - Pycharm: No module named sklearn - Stack Overflow
But although scikit-learn (which contains sklearn, that was initially confusing) seems fully installed on my system, including "import sklearn" working outside of PyCharm, I could not get the "import sklearn" to succeed inside PyCharm I finally got a python-expert friend to help me
- How to save a trained model by scikit-learn? [duplicate]
Closed 4 years ago I am trying to re-create the prediction of a trained model but I don't know how to save a model For example, I want to save the trained Gaussian processing regressor model and recreate the prediction after I trained the model The package I used to train model is scikit-learn
- Find p-value (significance) in scikit-learn LinearRegression
Find p-value (significance) in scikit-learn LinearRegression Asked 10 years, 10 months ago Modified 2 years, 1 month ago Viewed 443k times
- python - scikit-surprise installation in windows - Stack Overflow
Installation of scikit-surprise using pip in my project file, gives me an error like 'Error originates from subprocess' Then I tried to install it globally using conda
- Get confidence interval from sklearn linear regression in python
If you're looking to compute the confidence interval of the regression parameters, one way is to manually compute it using the results of LinearRegression from scikit-learn and numpy methods The code below computes the 95%-confidence interval (alpha=0 05) alpha=0 01 would compute 99%-confidence interval etc
- How to use pandas DataFrames with sklearn? - Stack Overflow
Like mentioned in the comments below your question your features and your label are persumably strings However, sklearn requires them to be numeric (sklearn is normally used with numpy arrays) If this is the case you have to convert the elements of your dataframe from strings to numeric values Looking at your code I assume that each element of your feature column is a list of strings and
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