/usr/bin/env python # -*- coding:utf-8 -*- import numpy as py import operator from 数据分类 import train_test_split import pandas as pd from sklearn import datasets '''载入鸢尾花数据集''' iris = pd.read_csv('iris.data', header=None) X = iris.iloc[0:150, 0:4].values y = ...
from sklearn.model_selection import train_test_split X,y = mglearn.datasets.make_forge() print('自变量前五行:\n{}'.format(X[:5])) print('因变量:{}'.format(y)) 1. 2. 3. 4. 5. 6. 自变量前五行: [[ 9.96346605 4.59676542] [11.0329545 -0.16816717] [11.54155807 5.21116083] [ 8.692890...
from sklearn.linear_modelimport LogisticRegression from sklearn.treeimport DecisionTreeClassifier from sklearn.neighborsimport KNeighborsClassifier from sklearn.discriminant_analysisimport LinearDiscriminantAnalysis from sklearn.naive_bayesimport GaussianNB from sklearn.svmimport SVC #训练数据 url ="F:/work/...
results.sort(key=operator.itemgetter(1))foriinrange(k): neighbors.append(results[i][0])returnneighbors#获取结果defgetNeighborsLabel(neighbors):'''输入最近邻点,求取其所属分类 :param neighbors:K个最近邻点 :return result:判定数据所属类'''results={}forrowinneighbors:ifrow[-1]notinresults.keys(...
利用Python实现KNN算法,完成鸢尾花分类任务,实现步骤: 1. 数据集的准备。 (1)使用SCIKIT-LEARN的自带的鸢尾花数据集,获取数据集的后两个特征,形成原始数据集D。 (2)待决策样本集D1的产生:在原始二维特征空间,基于该数据集的两种特征取值的最小值、最大值,获取该数据集的矩形包围盒,并在该的矩形区域上下左右...
语言:Python 工具:Jupyter Notebook 数据:这里直接引入sklearn(机器学习库)里的示例数据集:iris鸢尾花。其中,1-4列均为其特征属性,分别指的是sepal length(cm),sepal width(cm),petal length(cm),petal width(cm)。第5列指的是分类结果(离散值),其中0代表setosa花,1代表versicolor花,2代表virginica花。显然,...
目录 收起 【python ML系列】 knn KNeighborsClassifier 最近邻算法选项用法示例详解 sklearn.neighbors.K...
Currently I'm doing a project which may require using a kNN algorithm to find the top k nearest neighbors for a given point, say P. im using python, sklearn package to do the job, but our predefined metric is not one of those default metrics. so I have to use the user defined me...
Python sklearn.model_selection提供了K-fold,Stratified k-fold。 【scikit-learn】交叉验证及其用于参数选择、模型选择、特征选择的例子 - CSDN博客。 不同于k-fold的是,stratified采用分层采样。 推荐模块cross_val_score(sklearn.model_selection.cross_val_score - scikit-learn 0.19.1 documentation)。对于分类问...
Then I usedsklearnto train and test (after splitting the dataset 80% for train and 20% for the test). Code sample is given below: classifiers = [ SVC(), KNeighborsClassifier(n_neighbors=5)] X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2...