Machine learning with logistic regression

You are familiar with the steps of training and testing a classifier. With logistic regression, we will do the following:

• Load data into feature and target arrays, X and y, respectively
• Split the data into training and testing sets
• Train the logistic regression classifier on the training set
• Test the performance of the classifier on the test set

Getting ready

Define X, y – the feature and target arrays

Let's start predicting with scikit-learn's logistic regression. Perform the necessary imports and set the input variables X and the target variable y:

import numpy as np
import pandas as pd

X = all_data[feature_names]
y = all_data['target']

How to do it...

Provide training and testing sets

1. Import train_test_split to create testing and training sets for both X and y: the inputs and target. Note the stratify=y, which stratifies the categorical variable y. This means that there are the same proportions of zeros and ones in both y_train and y_test:

from sklearn.model_selection...