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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:  Load Digits Dataset
Step2:  Create Pipeline
Step3:  Create k-Fold Cross-Validation
Step4:  Conduct k-Fold Cross-Validation
Step5:  Calculate Mean Performance Score
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Python Code:
# Load libraries
import numpy as np
from sklearn import datasets
from sklearn import metrics
from sklearn.model_selection import KFold, cross_val_score
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
Explanation: Title: Cross-Validation
Slug: cross-validaton
Summary: How to cross-validate models for machine learning in Python.  
Date: 2017-09-15 12:00
Category: Machine Learning
Tags: Model Evaluation
Authors: Chris Albon
Preliminaries
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# Load the digits dataset
digits = datasets.load_digits()
# Create the features matrix
X = digits.data
# Create the target vector
y = digits.target
Explanation: Load Digits Dataset
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# Create standardizer
standardizer = StandardScaler()
# Create logistic regression
logit = LogisticRegression()
# Create a pipeline that standardizes, then runs logistic regression
pipeline = make_pipeline(standardizer, logit)
Explanation: Create Pipeline
End of explanation
# Create k-Fold cross-validation
kf = KFold(n_splits=10, shuffle=True, random_state=1)
Explanation: Create k-Fold Cross-Validation
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# Do k-fold cross-validation
cv_results = cross_val_score(pipeline, # Pipeline
                             X, # Feature matrix
                             y, # Target vector
                             cv=kf, # Cross-validation technique
                             scoring="accuracy", # Loss function
                             n_jobs=-1) # Use all CPU scores
Explanation: Conduct k-Fold Cross-Validation
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# Calculate mean
cv_results.mean()
Explanation: Calculate Mean Performance Score
End of explanation
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