使用K近邻算法，得出预测的分类准确率约97%
1. 加载数据
from sklearn.datasets import load_iris
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import mglearn
iris_dataset=load_iris()
2.散点图观察数据
from sklearn.model_selection import train_test_split
X_train,X_test,y_train,y_test=train_test_split(iris_dataset['data'],iris_dataset['target'],random_state=0)
iris_dataframe=pd.DataFrame(X_train,columns=iris_dataset.feature_names)
#散点图矩阵图，观察特征之间的关系
grr=pd.scatter_matrix(iris_dataframe,c=y_train,figsize=(20,20),marker='o',hist_kwds={'bins':20},
s=60,alpha=.8,cmap=mglearn.cm3)
image.png
3.构造K近邻算法
from sklearn.neighbors import KNeighborsClassifier
knn=KNeighborsClassifier(n_neighbors=1)
knn.fit(X_train,y_train)
4.评估模型
y_pred=knn.predict(X_test)
test_score=np.mean(y_pred==y_test)
print(y_pred)
print('accuracy is :{:.2f}'.format(test_score))
Out:
> [2 1 0 2 0 2 0 1 1 1 2 1 1 1 1 0 1 1 0 0 2 1 0 0 2 0 0 1 1 0 2 1 0 2 2 1 0 2]
> accuracy is :0.97
