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, …

Scikit-Learn itself provides very good classes to handle categorical data. Instead of writing your custom function, you should use LabelEncoder which is specially designed for this purpose.

9 As Alex stated, you need use to the full name of the module, uppercase is indifferent in this case. Both pip install scikit-learn==0.21.3 or pip install Scikit-learn==0.21.3 will work as I just tested it and I got a …

I am building code on python using skimage. But I am getting import errors while using skimage.segmentation. Traceback (most recent call last): File "superpixel.py", line 5, in …

Reason to use scikit-learn : scikit-learn contains less boilerplate than the tensorflow implementation. Reason to use tensorflow : If running on Nvidia GPU the algorithm will be run against in parallel , I'm …

2 Here is a function, printing rules of a scikit-learn decision tree under python 3 and with offsets for conditional blocks to make the structure more readable:

Pip seemed to picked that up and used it as the build system for scikit-image, causing the errors. Simply removing ninja from path solved my problem and scikit-image installed successfully.

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 …

Scikit-learns model.score (X,y) calculation works on co-efficient of determination i.e R^2 is a simple function that takes model.score= (X_test,y_test). It doesn't require y_predicted value to be supplied …

Scikit-learn provides two modules for Stratified Splitting: StratifiedKFold : This module is useful as a direct k-fold cross-validation operator: as in it will set up n_folds training/testing sets such that …