knn = KNN(x_train, y_train, x_test) predictions_labels = knn.knn_classify()print(confusion_matrix(y_test, predictions_labels))print(classification_report(y_test, predictions_labels)) endtime = datetime.datetime.now()print(endtime - starttime)#方法2 用KNN集成函数包clf0 = KNeighborsClassifie...
7.3 sklearn实现KNN算法 参考来源链接:https://blog.csdn.net/codedz/article/details/1088624981. sklearn中的KNN方法 sklearn.neighbors.KNeighborsClassifier(n_neighbors = 5, weights='uniform', algorithm = '', leaf_size = '30', p = 2, metric = 'minkowski', metric_params = None, n_jobs = ...
knn=neighbors.KNeighborsClassifier()#返回一个数据库 iris ---> 默认的参数#'filename': 'C:\\python3.6.3\\lib\\site-packages\\sklearn\\datasets\\data\\iris.csv'iris =datasets.load_iris()print(iris)#模型建立#data为特征值#target 为向量,每一行对应的分类,一维的模型knn.fit(iris.data, iris....
剪辑法 code: from sklearn import datasets import matplotlib.pyplot as plt from sklearn.model_selection import train_test_split from sklearn.neighbors import KNeighborsClassifier as KNN import numpy as np data,target = datasets.make_classification(n_samples=1000,n_features=2, n_informative=2,n_...
Scikit learn 也简称sklearn,是机器学习领域当中最知名的python模块之一。sklearn包含了很多机器学习的方式: Classification 分类; Regression 回归; Clustering 非监督分类; Dimensionality reduction 数据降维; Model Selection 模型选择; Preprocessing 数据与处理。 使用sklearn可以很方便地让...
from sklearn.metrics import classification_report from time import time '''KNN分类器''' ## 数据集读入、训练集与测试集样本划分及数据标准化: def Data_Input(): data = pd.read_csv("D:/Python/File/iris.csv") data.columns = ['sepal_length','sepal_width','petal_length','petal_width','...
from sklearn.metricsimportclassification_report from timeimporttime'''KNN分类器'''## 数据集读入、训练集与测试集样本划分及数据标准化: defData_Input():data=pd.read_csv("D:/Python/File/iris.csv")data.columns=['sepal_length','sepal_width','petal_length','petal_width','class']data.iloc[:...
Python实现代码如下: 代码语言:javascript 复制 defknnclassify(A,dataset,labels,k):datasetSize=dataset.shape[0]# 计算A点和当前点之间的距离 diffMat=tile(A,(datasetSize,1))-dataset sqDiffMat=diffMat**2sqDistances=sqDiffMat.sum(axis=1)distances=sqDistances**0.5# 按照增序对距离排序 ...
. predict rich_hat in 7001/10000, classification //基于3000个测试数据预测 (7000 missing values generated) . label values rich_hat rich_lb . tab rich rich_hat in 7001/10000 第四步,将模型预测结果和真实的测试数据对比,评估预测的准确度。
load(fr) #主函数 if __name__ == '__main__': #读取决策树 treeName = "D:\python_things\code\第2次作业\Page Blocks Classification Data Set\myTree.pkl" myTree = grabTree(treeName) #可视化决策树 createPlot(myTree) classification.py # -*- coding: UTF-8 -*- #Author:Yinli import...