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5 minutes to master machine learning iris logical regression classification
2022-07-06 14:35:00 【ブリンク】
This article will use 5 Minutes to help you master the most classic case of iris classification in machine learning .
sketch
Use scikit-learn library , coordination Numpy、Pandas It can make machine learning simple , Utilization based on Matplotlib Of seaborn Libraries make it easier to visualize .
First, import the library you want to use :
from sklearn import datasets
# We from sklearn You can get the data in your own data set
import numpy as np
import pandas as pd
import seaborn as sns
from sklearn.linear_model import LogisticRegression
# Use logistic regression to learn
from sklearn.model_selection import train_test_split
# Use it to segment data into training set and test set
Import data
sklearn We have prepared some data sets for practice , Including the iris data to be used now , We just need to use datasets Of l o a d i r i s ( ) load_iris() loadiris() The method can :
iris_data = datasets.load_iris()
Got iris_data yes sklearn Type included in , We can use i r i s . k e y s ( ) iris.keys() iris.keys() Method to see what it contains , He will return a dictionary :
>>> iris.keys()
dict_keys(['data', 'target', 'frame',
'target_names', 'DESCR', 'feature_names',
'filename', 'data_module'])
It includes 150 Group data ,data Indicates the included data ,target It means label , That is, what kind of iris this flower belongs to , The iris in the data has 3 Kind of setosa, versicolor and virginica, They are contained in target_names in , Indicates the name of the label .feature_names Indicates the name of the feature , That is, the description of the characteristics of iris , For example, there are petal lengths in the data set 、 Width and calyx length 、 Width . The rest is not used in this example , Don't introduce too much .
Next, extract the data and labels , And stored in Pandas Of DataFrame in ,:
>>> data = iris.data
>>> data = data.pd.DataFrame(data,columns = iris.target_names)
# Change the column name to the name of the feature
>>> data.head()
sepal length (cm) sepal width (cm) petal length (cm) petal width (cm)
0 5.1 3.5 1.4 0.2
1 4.9 3.0 1.4 0.2
2 4.7 3.2 1.3 0.2
3 4.6 3.1 1.5 0.2
4 5.0 3.6 1.4 0.2
Data visualization
Use seaborn Of p a i r p l o t ( ) pairplot() pairplot() Method can quickly view the relationship between each two variables , Including with themselves :
sns.pairplot(data)
model
Use sklearn The estimator of builds a logistic regression model :
modle = LogisticRegression()
Data preprocessing
First, all the data is processed into training set and test set , So that we can test the model , Use train_test_split() Method can easily do this , It will return separately x Training set ,x Test set ,y Training set ,y Test set :
x_train,x_test,y_train,y_test = train_test_split(X=data,y=iris.target,train_size=0.8)
# 80% As a training set The rest are used as test sets
Training models
Using estimators f i t ( ) fit() fit() The method can train the model :
model.fit(x_train,y_train)
Model to evaluate
Can be directly estimated s c o r e ( ) score() score() Methods calculate the score or accuracy of the model under the test set :
>>> model.score(x_test,y_test)
0.9333333333333333 # The accuracy rate has reached 93.33%, This is related to the division of training set and testing machine
Model to predict
The trained model can be used to predict the test machine data , in other words , When you know a set of data about the characteristics of iris , You can use this model to know which kind it belongs to :
>>> s = model.predict(x_test)
array([1, 2, 1, 2, 1, 0, 2, 1, 0,
0, 0, 2, 1, 0, 2, 0, 1, 2,
1, 1, 2, 2,1, 2, 0, 2, 1, 2, 0, 0])
# among 0 Express setosa,1 Express versicolor,2 Express virginica
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