Machine Learning with scikit-learn Quick Start Guide

Performance evaluation for classification algorithms

In order to evaluate the performance of classification, let's consider the two classification algorithms that we have built in this book: k-nearest neighbors and logistic regression.

The first step will be to implement both of these algorithms in the fraud detection dataset. We can do this by using the following code:

import pandas as pdfrom sklearn.model_selection import train_test_splitfrom sklearn.neighbors import KNeighborsClassifierfrom sklearn import linear_model#Reading in the fraud detection dataset df = pd.read_csv('fraud_prediction.csv')#Creating the features features = df.drop('isFraud', axis = 1).valuestarget = df['isFraud'].values#Splitting the data into training and test ...