Machine Learning with scikit-learn Quick Start Guide

Implementing the voting classifier in scikit-learn

In this section, you will learn how to implement the voting classifier in scikit-learn. The first step is to import the data, create the feature and target arrays, and create the training and testing splits. This can be done using the following code:

import pandas as pdfrom sklearn.model_selection import train_test_split#Reading in the datasetdf = pd.read_csv('fraud_prediction.csv')#Dropping the indexdf = df.drop(['Unnamed: 0'], axis = 1)#Splitting the data into training and test setsX_train, X_test, y_train, y_test = train_test_split(features, target, test_size = 0.3, random_state = 42)

Next, we will build two classifiers that include the voting classifier: the decision tree classifier ...