文章目录
前言
如下提供了两种训练方式：
- 常规训练 的话需要 自己去试那个K的值，一般试个 3、5、7、9 就行
- 网格搜索训练 可以让 机器自己去试这个K的值，训练结束后使用最好的模型预测即可
- N折交叉验证训练 会让训练量提升N倍，但是会最大化的利用已有数据进行训练和验证，一般来说折数多一些训练结果会变好，但也不宜过多，该方法 常用在数据量较少或者获取训练数据成本较高的情况
一、安装sklearn
python
pip install scikit-learn二、常规训练
python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
if __name__ == '__main__':
# 读取数据
iris = load_iris()
print(iris)
# {
# 'data': array([[5.1, 3.5, 1.4, 0.2], [4.9, 3. , 1.4, 0.2], ... [5.9, 3. , 5.1, 1.8]]),
# 'target': array([0, 0, ... 2]),
# 'frame': None,
# 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),
# 'DESCR': '.. _iris_dataset:\n\nIris plants dataset ...\n\n|details-end|',
# 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'],
# 'filename': 'iris.csv',
# 'data_module': 'sklearn.datasets.data'
# }
# 训练集、测试集拆分
x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=233, test_size=0.2)
print(x_train)
# [[4.8 3. 1.4 0.3]
# [6.3 3.3 4.7 1.6]
# ...
# [5.1 3.4 1.5 0.2]]
print(x_test)
# [[5.2 2.7 3.9 1.4]
# [6.8 3.2 5.9 2.3]
# ...
# [5.7 2.6 3.5 1. ]]
print(y_train)
# [0 1 ... 0]
print(y_test)
# [1 2 ... 1]
# 参数标准化【特征缩放到均值为0标准差为1】
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.fit_transform(x_test)
# 【常规训练】初始化KNN并指定K为5
model = KNeighborsClassifier(n_neighbors=5)
# 训练
model.fit(x_train, y_train)
# 评估
predict = model.predict(x_test)
print(predict)
# [1 2 ... 1]
# 打印评价指标
accuracy = model.score(x_test, y_test)
print(accuracy)
# 0.9666666666666667三、网格搜素训练+N折交叉验证
python
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
if __name__ == '__main__':
# 读取数据
iris = load_iris()
print(iris)
# {
# 'data': array([[5.1, 3.5, 1.4, 0.2], [4.9, 3. , 1.4, 0.2], ... [5.9, 3. , 5.1, 1.8]]),
# 'target': array([0, 0, ... 2]),
# 'frame': None,
# 'target_names': array(['setosa', 'versicolor', 'virginica'], dtype='<U10'),
# 'DESCR': '.. _iris_dataset:\n\nIris plants dataset ...\n\n|details-end|',
# 'feature_names': ['sepal length (cm)', 'sepal width (cm)', 'petal length (cm)', 'petal width (cm)'],
# 'filename': 'iris.csv',
# 'data_module': 'sklearn.datasets.data'
# }
# 训练集、测试集拆分
x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=233, test_size=0.2)
print(x_train)
# [[4.8 3. 1.4 0.3]
# [6.3 3.3 4.7 1.6]
# ...
# [5.1 3.4 1.5 0.2]]
print(x_test)
# [[5.2 2.7 3.9 1.4]
# [6.8 3.2 5.9 2.3]
# ...
# [5.7 2.6 3.5 1. ]]
print(y_train)
# [0 1 ... 0]
print(y_test)
# [1 2 ... 1]
# 参数标准化【特征缩放到均值为0标准差为1】
scaler = StandardScaler()
x_train = scaler.fit_transform(x_train)
x_test = scaler.fit_transform(x_test)
# # 【常规训练】初始化KNN并指定K为5
# model = KNeighborsClassifier(n_neighbors=5)
# 【网格搜索训练+N折交叉验证】初始化KNN随便指定一个K值，将 3, 5, 7, 9 分别进行4折交叉验证训练，总训练次数为 16 次
model = KNeighborsClassifier(n_neighbors=1)
model = GridSearchCV(model, param_grid={'n_neighbors': [3, 5, 7, 9]}, cv=4)
# 训练
model.fit(x_train, y_train)
# 评估【使用最优模型评估】
predict = model.best_estimator_.predict(x_test)
print(predict)
# [1 2 ... 1]
# 打印评价指标
print(f'best accuracy： {model.best_score_}')
print(f'results： {model.cv_results_}')
# accuracy = model.score(x_test, y_test)
# print(accuracy)
# # 0.9666666666666667