The first machine learning scikit-learn Visualization example

scikit-learn, and writing sklearn, python is a language-based machine learning tools open source packages. It is through NumPy, SciPy and
python Matplotlib numerical calculations such as libraries for efficient algorithm, and covers almost all major machine learning algorithms.
http://scikit-learn.org/stable/index.html

https://sklearn.apachecn.org/

Install the necessary packages:

pip install numpy pandas matplotlib scikit-learn  graphviz  scipy jupyter

This example runs jupyter, the copy directly to the jupyter in the run.

# -*- coding:utf-8 -*-
from sklearn import tree
from sklearn.datasets import load_wine
from sklearn.model_selection import train_test_split

wine = load_wine()
print(wine.data.shape)
print(wine.target)
#如果wine是一张表，应该长这样：
import pandas as pd
pd.concat([pd.DataFrame(wine.data),pd.DataFrame(wine.target)],axis=1)
print(wine.feature_names)
print(wine.target_names)
Xtrain, Xtest, Ytrain, Ytest = train_test_split(wine.data,wine.target,test_size=0.3)
print(Xtrain.shape)
Print (Xtest.shape)

clf  = tree.DecisionTreeClassifier (Criterion = " Entropy " ) 
CLF = clf.fit (Xtrain, Ytrain) 
Score = clf.score (Xtest, Ytest) # return prediction accuracy 
Print (Score) 

feature_name to = [ ' alcohol ' , ' malic acid ' , ' gray ' , ' gray basic ' , ' magnesium ' , ' total phenols ' , ' flavonoids ' , ' non-phenolic flavanoids ' , 'anthocyanin' ,' Color intensity ' , ' tone ' , ' OD280 / od315 diluted wine ' , ' proline ' ] 

Import Graphviz 
dot_data = tree.export_graphviz (CLF 
                               , feature_names = feature_name to 
                               , class_names = [ " Gin " , " Shirley " , " Bell Moder " ] 
                               , Filled = True 
                               ,rounded=True
                               )
graph = graphviz.Source(dot_data)
graph

operation result:

(178, 13)
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]
['alcohol', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols', 'flavanoids', 'nonflavanoid_phenols', 'proanthocyanins', 'color_intensity', 'hue', 'od280/od315_of_diluted_wines', 'proline']
['class_0' 'class_1' 'class_2']
(124, 13)
(54, 13)
0.9629629629629629

 

 

No jupyter students to see here: https://www.cnblogs.com/v5captain/p/6688494.html

Machine learning can not live without it, hey!