Many scikit-learn functions have a verbose argument that, according to their documentation, "[c]ontrols the verbosity: the higher, the more messages" (e.g., GridSearchCV). …

I am trying to run the following code for a brief machine learning algorithm: import re import argparse import csv from collections import Counter from sklearn import datasets import …

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 …

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 …

The syntax of the sklearn code can remain the same. Whenever equivalent cuML functions don't exist, it will fallback to using the sklearn implementation on CPU.

I am trying to design a simple Decision Tree using scikit-learn in Python (I am using Anaconda's Ipython Notebook with Python 2.7.3 on Windows OS) and visualize it as follows: from pandas …

Problem context Using scikit-learn with Python, I'm trying to fit a quadratic polynomial curve to a set of data, so that the model would be of the form y = a2x^2 + a1x + a0 and the an …

I want to plot a confusion matrix to visualize the classifer's performance, but it shows only the numbers of the labels, not the labels themselves: from sklearn.metrics import confusion_matrix …

Yes decision tree is able to handle both numerical and categorical data. Which holds true for theoretical part, but during implementation, you should try either OrdinalEncoder or one-hot …

I know that using train_test_split from sklearn.cross_validation, one can divide the data in two sets (train and test). However, I couldn't find any solution about splitting the data into three sets.