University of Tsukuba Machine Learning Course: Study sklearn while creating the Python script part of the task (17)
Last time University of Tsukuba Machine Learning Course: Study sklearn while creating the Python script part of the assignment (16) https://github.com/legacyworld/sklearn-basic
Challenge 8.7 Example of Principal Component Analysis
Commentary on Youtube: 9th (1) per 30 minutes I gave up on Task 8.3 because I couldn't reproduce Cluster 3 well.
The problem of principal component analysis of the usual iris data. As a program, scikit-learn is easy. Using scatter_matrix of pandas only for the graph part is a little different from before.
python:Homework_8.7.py
#Challenge 8.7 Example of principal component analysis
#Commentary on Youtube: 9th(1)Per 30 minutes
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.datasets import load_iris
from sklearn.decomposition import PCA
iris = load_iris()
pca = PCA()
X = iris['data']
y = iris['target']
#Principal component analysis
pca.fit(X)
transformed = pca.fit_transform(X)
#Contribution rate
print(pca.explained_variance_ratio_)
#drawing
fig, ax = plt.subplots()
iris_dataframe = pd.DataFrame(transformed, columns=[0,1,2,3])
Axes = pd.plotting.scatter_matrix(iris_dataframe, c=y, figsize=(50, 50),ax=ax)
plt.savefig("8.7.png ")
Contribution rate
[0.92461872 0.05306648 0.01710261 0.00521218]
Graph
The contribution rate of the first principal component is 0.92, and even if you look at the graph, you can see that the left end (first principal component) is clearly divided. Therefore, the setosa classifier can classify 92.5% with only the first main component, and even with four components, it does not go up so much, so only the first main component is sufficient.
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