python 机器学习交叉验证train_test_split 网格搜索GridSearchCV

交叉验证用于在数据集中拆分成测试集和验证集，用于模型训练和验证

网格搜索用于自动查找最合适的模型参数，用于模型调优

复制代码

from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, cross_val_score, GridSearchCV
from sklearn.metrics import accuracy_score
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
from sklearn.preprocessing import StandardScaler
import seaborn as sns
import pandas as pd

matplotlib.use('TkAgg')

iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.3, random_state=42)


scaler = StandardScaler()
X_train = scaler.fit_transform(X_train)

print(X_test)
X_test = scaler.transform(X_test)
print(X_test)

grid_params = {
    'n_neighbors': [3, 5, 7, 9],
    'weights': ['uniform', 'distance'],
    'metric': ['euclidean', 'manhattan']
}
knn = KNeighborsClassifier(n_neighbors=3)
grid_search = GridSearchCV(knn, grid_params, cv=5)
grid_search.fit(X_train, y_train)
print("最佳参数:", grid_search.best_params_)
print("最佳交叉验证得分:", grid_search.best_score_)
print("测试集准确率:", grid_search.score(X_test, y_test))


# plt.figure(figsize=(10, 6))
# plt.scatter(X_test[:, 0], X_test[:, 1], c=y_pred, cmap='viridis')
# plt.xlabel('Sepal length')
# plt.ylabel('Sepal width')
# plt.title('KNN Classification')
# plt.show()
#
# print(accuracy_score(y_test, y_pred))
# print(cross_val_score(knn, iris.data, iris.target, cv=5))

F:\Users\FRT\anaconda3\envs\test\python.exe E:\PycharmProjects\pythonProject\3.py
$\[6.1 2.8 4.7 1.2$ $5.7 3.8 1.7 0.3$ $7.7 2.6 6.9 2.3$ $6. 2.9 4.5 1.5$ $6.8 2.8 4.8 1.4$ $5.4 3.4 1.5 0.4$ $5.6 2.9 3.6 1.3$ $6.9 3.1 5.1 2.3$ $6.2 2.2 4.5 1.5$ $5.8 2.7 3.9 1.2$ $6.5 3.2 5.1 2.$ $4.8 3. 1.4 0.1$ $5.5 3.5 1.3 0.2$ $4.9 3.1 1.5 0.1$ $5.1 3.8 1.5 0.3$ $6.3 3.3 4.7 1.6$ $6.5 3. 5.8 2.2$ $5.6 2.5 3.9 1.1$ $5.7 2.8 4.5 1.3$ $6.4 2.8 5.6 2.2$ $4.7 3.2 1.6 0.2$ $6.1 3. 4.9 1.8$ $5. 3.4 1.6 0.4$ $6.4 2.8 5.6 2.1$ $7.9 3.8 6.4 2.$ $6.7 3. 5.2 2.3$ $6.7 2.5 5.8 1.8$ $6.8 3.2 5.9 2.3$ $4.8 3. 1.4 0.3$ $4.8 3.1 1.6 0.2$ $4.6 3.6 1. 0.2$ $5.7 4.4 1.5 0.4$ $6.7 3.1 4.4 1.4$ $4.8 3.4 1.6 0.2$ $4.4 3.2 1.3 0.2$ $6.3 2.5 5. 1.9$ $6.4 3.2 4.5 1.5$ $5.2 3.5 1.5 0.2$ $5. 3.6 1.4 0.2$ $5.2 4.1 1.5 0.1$ $5.8 2.7 5.1 1.9$ $6. 3.4 4.5 1.6$ $6.7 3.1 4.7 1.5$ $5.4 3.9 1.3 0.4$ $5.4 3.7 1.5 0.2\]$ $\[ 0.3100623 -0.50256349 0.484213 -0.05282593$ $-0.17225683 1.89603497 -1.26695916 -1.27039917$ $2.23933883 -0.98228318 1.76840592 1.43531914$ $0.18948252 -0.26270364 0.36746819 0.35303182$ $1.15412078 -0.50256349 0.54258541 0.2177459$ $-0.53399618 0.93659559 -1.38370397 -1.13511325$ $-0.29283662 -0.26270364 -0.15788346 0.08245999$ $1.27470056 0.21701605 0.71770262 1.43531914$ $0.43064208 -1.94172256 0.36746819 0.35303182$ $-0.05167705 -0.74242333 0.01723376 -0.05282593$ $0.79238143 0.4568759 0.71770262 1.0294614$ $-1.25747488 -0.02284379 -1.44207638 -1.540971$ $-0.4134164 1.17645543 -1.50044878 -1.40568508$ $-1.1368951 0.21701605 -1.38370397 -1.540971$ $-0.89573553 1.89603497 -1.38370397 -1.27039917$ $0.55122187 0.69673574 0.484213 0.48831773$ $0.79238143 -0.02284379 1.12630946 1.30003323$ $-0.29283662 -1.22214302 0.01723376 -0.18811184$ $-0.17225683 -0.50256349 0.36746819 0.08245999$ $0.67180165 -0.50256349 1.00956465 1.30003323$ $-1.37805466 0.4568759 -1.32533157 -1.40568508$ $0.3100623 -0.02284379 0.60095781 0.75888956$ $-1.01631531 0.93659559 -1.32533157 -1.13511325$ $0.67180165 -0.50256349 1.00956465 1.16474731$ $2.4804984 1.89603497 1.47654389 1.0294614$ $1.033541 -0.02284379 0.77607503 1.43531914$ $1.033541 -1.22214302 1.12630946 0.75888956$ $1.15412078 0.4568759 1.18468187 1.43531914$ $-1.25747488 -0.02284379 -1.44207638 -1.27039917$ $-1.25747488 0.21701605 -1.32533157 -1.40568508$ $-1.49863445 1.41631528 -1.675566 -1.40568508$ $-0.17225683 3.33519404 -1.38370397 -1.13511325$ $1.033541 0.21701605 0.30909579 0.2177459$ $-1.25747488 0.93659559 -1.32533157 -1.40568508$ $-1.73979401 0.4568759 -1.50044878 -1.40568508$ $0.55122187 -1.22214302 0.65933022 0.89417548$ $0.67180165 0.4568759 0.36746819 0.35303182$ $-0.77515575 1.17645543 -1.38370397 -1.40568508$ $-1.01631531 1.41631528 -1.44207638 -1.40568508$ $-0.77515575 2.6156145 -1.38370397 -1.540971$ $-0.05167705 -0.74242333 0.71770262 0.89417548$ $0.18948252 0.93659559 0.36746819 0.48831773$ $1.033541 0.21701605 0.484213 0.35303182$ $-0.53399618 2.13589481 -1.50044878 -1.13511325$ $-0.53399618 1.65617512 -1.38370397 -1.40568508\]$
最佳参数: {'metric': 'euclidean', 'n_neighbors': 3, 'weights': 'uniform'}

最佳交叉验证得分: 0.9428571428571428

测试集准确率: 1.0

Process finished with exit code 0

python 机器学习 交叉验证train_test_split 网格搜索GridSearchCV

python 机器学习交叉验证train_test_split 网格搜索GridSearchCV