【学习打卡】第9天 Python3入门机器学习

课程介绍

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课程名称：Python3入门机器学习 经典算法与应用 入行人工智能
课程章节：4-5；4-6
主讲老师：liuyubobobo

内容导读

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• 第一部分 超参数的选择
• 第二部分 网格搜索

课程详细

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第一部分 超参数的选择

运行机器学习算法之前需要确定的参数如KNN的K
模型参数:在算法过程中学习的参数
如何寻找好的超参数

• 领域知识
• 经验数值
• 实验搜索

使用sklearn 来学习

#老生常谈的内容，调用机器学习算法的函数
import numpy as np
from sklearn import datasets
digits = datasets.load_digits()
X = digits.data
y = digits.target
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=666)
from sklearn.neighbors import KNeighborsClassifier

knn_clf = KNeighborsClassifier(n_neighbors=3)
knn_clf.fit(X_train, y_train)
knn_clf.score(X_test, y_test)

超参数K

就是在一开始输入的参数K值

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)

超参数距离

算法自己有封装的，意思就是考虑距离的权重

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)

明可夫斯基距离相应的p

这个算法欧里几合的距离，其实是p等于2，解释起来有点麻烦

%%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)

第二部分 网格搜索

参数与参数之间其实是有相互关联的，那么怎么能在一次运算得出最好的超参数的组合呢.tklearn中已经封装了一个直接使用网格计算超参数

#老生常谈的内容，调用机器学习算法的函数
import numpy as np
from sklearn import datasets
digits = datasets.load_digits()
X = digits.data
y = digits.target
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2,random_state=666)
from sklearn.neighbors import KNeighborsClassifier

knn_clf = KNeighborsClassifier(n_neighbors=3)
knn_clf.fit(X_train, y_train)
knn_clf.score(X_test, y_test)

#先定义一个参数，数组
#这里一共搜索两组数据，
param_grid = [
    {
        'weights': ['uniform'], 
        'n_neighbors': [i for i in range(3, 11)]
    },
    {
        'weights': ['distance'],
        'n_neighbors': [i for i in range(1, 11)], 
        'p': [i for i in range(1, 6)]
    }
]

knn_clf = KNeighborsClassifier()

from sklearn.model_selection import GridSearchCV

grid_search = GridSearchCV(knn_clf, param_grid)
%%time
grid_search.fit(X_train, y_train)
knn_clf = grid_search.best_estimator_
knn_clf.score(X_test,y_test)
%%time
#n_jobs用几核来进行运算，
#verbose输入数字来判断计算的时候有一些数据输出的多少
grid_search = GridSearchCV(knn_clf, param_grid, n_jobs=-1, verbose=2)
grid_search.fit(X_train, y_train)

课程思考

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对于各种各样的超参数的选择有点让人迷糊，这时候最重要的就是找准主次，就KNN算法而言，我认为影响准确性比重大的为K，另外有一个值得的注意的问题就是过拟合，现在还是有点不明朗

课程截图