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Given the following text description, write Python code to implement the functionality described below step by step
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Description:
 Title
Step1:  Generate Features And Target Data
Step2:  Create Logistic Regression
Step3:  Cross-Validate Model Using F1
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Python Code:
# Load libraries
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.datasets import make_classification
Explanation: Title: F1 Score
Slug: f1_score
Summary: How to evaluate a Python machine learning using F1 score.  
Date: 2017-09-15 12:00
Category: Machine Learning
Tags: Model Evaluation
Authors: Chris Albon
<a alt="F1 Score" href="https://machinelearningflashcards.com">
    <img src="f1_score/F1_Score_print.png" class="flashcard center-block">
</a>
Preliminaries
End of explanation
# Generate features matrix and target vector
X, y = make_classification(n_samples = 10000,
                           n_features = 3,
                           n_informative = 3,
                           n_redundant = 0,
                           n_classes = 2,
                           random_state = 1)
Explanation: Generate Features And Target Data
End of explanation
# Create logistic regression
logit = LogisticRegression()
Explanation: Create Logistic Regression
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# Cross-validate model using precision
cross_val_score(logit, X, y, scoring="f1")
Explanation: Cross-Validate Model Using F1
End of explanation
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