Python3入门机器学习经典算法与应用——超参数

绝地灬酷狼 2022-12-20 11:27 86阅读 0赞

# 超参数 #

knn算法中，k值就是一个典型的超参数

## 超参数 ##

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)

0.9888888888888889

### 寻找最好的k ###

best_score = 0.0
    best_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)

best_k = 4
    best_score = 0.9916666666666667

### 考虑距离？不考虑距离？ ###

best_score = 0.0
    best_k = -1
    best_method = ""
    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_method =", best_method)
    print("best_k =", best_k)
    print("best_score =", best_score)

best_method = uniform
    best_k = 4
    best_score = 0.9916666666666667

sk_knn_clf = KNeighborsClassifier(n_neighbors=4, weights="distance", p=1)
    sk_knn_clf.fit(X_train, y_train)
    sk_knn_clf.score(X_test, y_test)

0.9833333333333333

### 搜索明可夫斯基距离相应的p ###

best_score = 0.0
    best_k = -1
    best_p = -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_p = p
                best_score = score
            
    print("best_k =", best_k)
    print("best_p =", best_p)
    print("best_score =", best_score)

best_k = 3
    best_p = 2
    best_score = 0.9888888888888889