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 …

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 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 …

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.

How do I convert data from a Scikit-learn Bunch object to a Pandas DataFrame? from sklearn.datasets import load_iris import pandas as pd data = load_iris () print (type (data)) …

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 …

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 …

from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve import matplotlib.pyplot as plt import seaborn as sns import numpy as np def plot_ROC(y_train_true, …

Since scikit-learn 0.22, sklearn defines a sklearn.inspection module which implements permutation_importance, which can be used to find the most important features - higher value …

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 = …