KNN

最新推荐文章于 2021-08-11 20:10:58 发布

小雨林夕

最新推荐文章于 2021-08-11 20:10:58 发布

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分类专栏：学习笔记

本文链接：https://blog.youkuaiyun.com/qq_42283960/article/details/86164905

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scikit-learn中的KNN

from sklearn.neighbors import KNeighborsClassifier
kNN_classifier=KNeighborsClassifier(n_neighbors=k)
kNN_classifier.fit(x_train,y_train)
x_predict=x.reshape(1,-1)             #输入变为矩阵 
kNN_classifier.predict(x_predict)

train_test_split

训练测试数据划分

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)

accuracy_score

from sklearn.metrics import accuracy_score
accuracy_score(y_test,y_predict)

knn_clf.score(x_test,y_test)

超参数

knn是否考虑距离参数 weights=uniform/distance
超参数p p范数

Grid Search

from sklearn.model_selection import GridSearchCV
grid_search=GridSearchCV(knn_clf,param_grid)
grid_search.fit(x_train,y_train)
grid_search.best_estimator_            
grid_search.best_score_
grid_search.best_params_

grid_search=GridSearchCV(knn_clf,param_grid,n_jobs=4，verbose=2)
#计算机分配几个核计算，n_jobs=-1，所有核。
verbose搜索过程中输出，越大越详细

数据归一化

最值归一化
均值方差归一化
StandardScaler

from sklearn.preprocessing import StandardScaler
standardScaler=StandardScaler()
standardScaler.fit(X_train)
standardScaler.mean_        #均值
standardScaler.std_            #standardScaler.scale_
X_train=standardScaler.transform(X_train)
X_test_standard=standardScaler.transform(X_test)