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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
- ImportError: No module named sklearn (Python) - Stack Overflow
I wanna use scikit-learn I have typed pip install -U scikit-learn pip3 install sklearn to install it; but when i type $ Python gt; gt; gt; import sklearn it returns ImportError: No module na
- pip install sklearn: Cannot install sklearn - Stack Overflow
As stated in the error: 'sklearn' PyPI package is deprecated, use 'scikit-learn' rather than 'sklearn' for pip commands Therefore try: pip install scikit-learn
- ModuleNotFoundError: No module named sklearn - Stack Overflow
When I run: from sklearn import datasets I get the error: ModuleNotFoundError: No module named 'sklearn' How can I solve this?
- Stratified Train Test-split in scikit-learn - Stack Overflow
X, Xt, userInfo, userInfo_train = sklearn cross_validation train_test_split(X, userInfo) However, I'd like to stratify my training dataset How do I do that? I've been looking into the StratifiedKFold method, but doesn't let me specifiy the 75% 25% split and only stratify the training dataset
- ImportError: cannot import name joblib from sklearn. externals
I am trying to load my saved model from s3 using joblib import pandas as pd import numpy as np import json import subprocess import sqlalchemy from sklearn externals import joblib ENV = 'dev' mod
- Parameter stratify from method train_test_split (scikit Learn)
I am trying to use train_test_split from package scikit Learn, but I am having trouble with parameter stratify Hereafter is the code: from sklearn import cross_validation, datasets X = iris data
- What does the fit method in scikit-learn do? - Stack Overflow
In a nutshell: fitting is equal to training Then, after it is trained, the model can be used to make predictions, usually with a predict() method call To elaborate: Fitting your model to (i e using the fit() method on) the training data is essentially the training part of the modeling process It finds the coefficients for the equation specified via the algorithm being used (take for
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