一、原理
如果一个样本在特征空间中 k个最相似(即特征空间中最邻近) 的样本中大多数属于某个类别，则该样本也属于这个类别
二、鸢尾花种类预测
1、获取数据
导sklearn原有的数据集
from sklearn.datasets import load_iris
iris = load_iris()
2、数据集划分
from sklearn.model_selection import train_test_split
x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=6)
3、特征工程(标准化)
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
4、KNN预估器流程
estimator = KNeighborsClassifier(n_neighbors=3)
estimator.fit(x_train, y_train)
5、模型评估
y_predict = estimator.predict(x_test)
print("y_predict:\n", y_predict)
print("直接比对真实值和预测值", y_test == y_predict)
score = estimator.score(x_test, y_test)
print("准确率:\n", score)
6、完整代码
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.neighbors import KNeighborsClassifier
def knn_irs():
"""
knn算法对鸢尾花分类
:return:
"""
iris = load_iris()
x_train, x_test, y_train, y_test = train_test_split(iris.data, iris.target, random_state=28)
transfer = StandardScaler()
x_train = transfer.fit_transform(x_train)
x_test = transfer.transform(x_test)
estimator = KNeighborsClassifier(n_neighbors=3)
estimator.fit(x_train, y_train)
y_predict = estimator.predict(x_test)
print("y_predict:\n", y_predict)
print("直接比对真实值和预测值", y_test == y_predict)
score = estimator.score(x_test, y_test)
print("准确率:\n", score)
return None
if __name__ == '__main__':
knn_irs()
三、K-近邻算法总结
优点：简单、易于理解、易于实现、无需训练
缺点：
1、必须指定K值，K值选择不当，则分类精度不能保障
2、懒惰算法，计算量大，开销大
四、模型选择与调优
交叉验证：将数据分为训练和验证集
例：将数据分成4份，其中一份作为验证集，然后经过4次测试，每次都更换不同的验证集。即得到四组模型的结果，取平均值作为最终结果。
使用交叉验证改进鸢尾花预测
param_dict = {"n_neighbors": [1, 3, 5, 7, 9, 11]}
estimator = GridSearchCV(estimator, param_grid=param_dict, cv=10)
estimator.fit(x_train, y_train)
y_predict = estimator.predict(x_test)
print("y_predict:\n", y_predict)
print("直接比对真实值和预测值", y_test == y_predict)
score = estimator.score(x_test, y_test)
print("准确率:\n", score)
print("最佳参数:\n", estimator.best_params_)
print("最佳结果:\n", estimator.best_score_)
print("最佳估计器:\n", estimator.best_estimator_)
print("交叉验证结果:\n", estimator.cv_results_)
y_predict:
[0 1 1 0 2 1 2 1 1 0 2 0 1 1 2 0 2 2 2 1 0 0 1 2 1 0 2 2 0 1 0 2 1 0 2 1 2
1]
直接比对真实值和预测值 [ True False True True True True True True True True True True
True True True True True True True True True True True False
True True True True True True True True True True False True
True True]
准确率:
0.9210526315789473
最佳参数:
{'n_neighbors': 3}
最佳结果:
0.9553030303030303
最佳估计器:
KNeighborsClassifier(n_neighbors=3)
交叉验证结果:
{'mean_fit_time': array([0.00030003, 0.00030031, 0.00040004, 0.00030005, 0.00030005,
0.00040019]), 'std_fit_time': array([0.0004583 , 0.00045873, 0.00048995, 0.00045833, 0.00045833,
0.00049013]), 'mean_score_time': array([0.00060017, 0.00089998, 0.0006 , 0.00080037, 0.00069995,
0.00070009]), 'std_score_time': array([0.00049004, 0.00029999, 0.0004899 , 0.00040019, 0.00045822,
0.00045832]), 'param_n_neighbors': masked_array(data=[1, 3, 5, 7, 9, 11],
mask=[False, False, False, False, False, False],
fill_value='?',
dtype=object), 'params': [{'n_neighbors': 1}, {'n_neighbors': 3}, {'n_neighbors': 5}, {'n_neighbors': 7}, {'n_neighbors': 9}, {'n_neighbors': 11}], 'split0_test_score': array([0.83333333, 0.91666667, 0.91666667, 0.91666667, 0.91666667,
0.91666667]), 'split1_test_score': array([1., 1., 1., 1., 1., 1.]), 'split2_test_score': array([0.90909091, 0.90909091, 0.90909091, 0.90909091, 0.90909091,
0.90909091]), 'split3_test_score': array([1. , 1. , 0.90909091, 0.90909091, 0.90909091,
0.90909091]), 'split4_test_score': array([0.72727273, 0.72727273, 0.72727273, 0.72727273, 0.72727273,
0.72727273]), 'split5_test_score': array([1., 1., 1., 1., 1., 1.]), 'split6_test_score': array([1. , 1. , 0.81818182, 0.81818182, 1. ,
1. ]), 'split7_test_score': array([1. , 1. , 1. , 0.90909091, 1. ,
1. ]), 'split8_test_score': array([0.90909091, 1. , 1. , 1. , 1. ,
1. ]), 'split9_test_score': array([1., 1., 1., 1., 1., 1.]), 'mean_test_score': array([0.93787879, 0.95530303, 0.9280303 , 0.91893939, 0.94621212,
0.94621212]), 'std_test_score': array([0.0894966 , 0.08343314, 0.08894662, 0.08571326, 0.08301938,
0.08301938]), 'rank_test_score': array([4, 1, 5, 6, 2, 2])}
Process finished with exit code 0
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