第4章 最基础的分类算法-k近邻算法

思想极度简单

应用数学知识少

效果好（缺点？）

可以解释机器学习算法使用过程中的很多细节问题

更完整的刻画机器学习应用的流程

distances = []
for x_train in X_train:
    d=sqrt(np.sum((x_train-x)**2))
    distances.append(d)

distances=[sqrt(np.sum((x_train-x)**2)) for x_train in X_train]

可以说kNN是一个不需要训练过程的算法

K近邻算法是非常特殊的，可以被认为是没有模型的算法

为了和其他算法统一，可以认为训练数据集就是模型本身

kNN：
from sklearn.neighbors import KNeighborsClassifier
kNN_classifier=KNeighborsClassifier(n_neighbors=6)
kNN_classifier.fit(X_train,y_train)
kNN_classifier.predict(x)

有关K近邻算法

解决分类问题

天然可以解决多分类问题

思想简单，效果强大

使用k近邻算法解决回归问题

KNeighborsRegressor

kNN：
from sklearn.neighbors import KNeighborsClassifier
kNN_classifier=KNeighborsClassifier(n_neighbors=6)
kNN_classifier.fit(X_train,y_train)
kNN_classifier.predict(x)

须考虑距离的权重！通常是将距离的倒数作为权重

相当于因为距离又获得了一个超参数

寻找最好的k,调参
best_score = 0.0
besk_k = -1
for k in range(1,11):
    knn_clf = KNeighborsClassifier(n_neighbors=k)
    knn_clf.fit(X_train,y_train)
    score = knn_clf.score(X_test,y_test)
    if score>best_score:
        best_k=k
        best_score=score

print('best_k=',best_k)
print('best_score=',best_score)

考虑距离？
best_method = ''
best_score = 0.0
besk_k = -1
for method in ['uniform','distance']:
    for k in range(1,11):
        knn_clf = KNeighborsClassifier(n_neighbors=k,weights=method)
        knn_clf.fit(X_train,y_train)
        score = knn_clf.score(X_test,y_test)
        if score>best_score:
            best_k=k
            best_score=score
            best_method = method
print('best_k=',best_k)
print('best_score=',best_score)
print('best_method',best_method)

搜索明可夫斯基距离相应的p
%%time
best_p = -1
best_score = 0.0
besk_k = -1
for k in range(1,11):
    for p in range(1,6):
        knn_clf = KNeighborsClassifier(n_neighbors=k,weights='distance',p = p)
        knn_clf.fit(X_train,y_train)
        score = knn_clf.score(X_test,y_test)
        if score>best_score:
            best_k=k
            best_score=score
            best_p=p
print('best_k=',best_k)
print('best_score=',best_score)
print('best_p=',best_p)

缺点2：高度数据相关

缺点3:预测的结果不具有可解释性

缺点4：维数灾难

随着维度的增加，‘看似相近’的的两个点之间的距离越来越大

解决方法：降维（PCA）

# coding=utf-8
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score  # 分类的准确度
from sklearn.model_selection import GridSearchCV

iris = datasets.load_iris()
X = iris.data
y = iris.target
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=666)
standardScaler = StandardScaler()  # 创建实例
standardScaler.fit(X_train)
# standardScaler.mean_
# standardScaler.scale_
X_train = standardScaler.transform(X_train)  # 使用transform方法进行归一化
X_test_standard = standardScaler.transform(X_test)

# 寻找最好的参数K
# param_grid = [
#     {
#         'weights': ['uniform'],
#         'n_neighbors': [i for i in range(1, 11)]
#     },
#     {
#         'weights': ['distance'],
#         'n_neighbors': [i for i in range(1, 11)],
#         'p': [i for i in range(1, 6)]
#     }
# ]
# knn_clf = KNeighborsClassifier()
# grid_search = GridSearchCV(knn_clf, param_grid)
# grid_search.fit(X_train, y_train)
# print(grid_search.best_estimator_, grid_search.best_params_, grid_search.best_score_)
# knn_clf.predict(X_test)
# knn_clf.score(X_test, y_test)

knn_clf = KNeighborsClassifier(n_neighbors=3)
knn_clf.fit(X_train, y_train)  # X_train已经进行了归一化
print(knn_clf.score(X_test_standard, y_test))
# 或者
y_predict = knn_clf.predict(X_test_standard)
print(accuracy_score(y_test, y_predict))
knn_clf.score(X_test_standard, y_test)

个人整个流程代码