对于这些异常值,可以利用sklearn中的EllipticEnvelope进行识别,利用KNNImputer进行快速处理。 话不多说,上代码: # 以回归数据集为例 from sklearn.datasets import make_regression data,_ = make_regression(n_samples=10, # 这里仅需要x变量进行展示 n_features=3, n_targets=1) data # data输出结果 array([...
sklearn.imputute子包包括两种基于模型的估算算法--KNNImputer和IterativeImputer。 顾名思义,KNNImputer使用k-Nearest-Neighbors算法为缺失值找到最佳替代值: fromsklearn.imputeimportKNNImputer # Code taken from Sklearn user guide X = [[1,2, np.nan], [3,4,3], [np.nan,6,5], [8,8,7]] impute...
KNNImputer使用k-最近邻算法来找到缺失值的最佳替代值: from sklearn.impute import KNNImputer # Code taken from Sklearn user guide X = [[1, 2, np.nan], [3, 4, 3], [np.nan, 6, 5], [8, 8, 7]] imputer = KNNImputer(n_neighbors=2) imputer.fit_transform(X) IterativeImputer是更健壮...
#用KNNImputer 填充 Insulin SkinThickness corr_SkinThickness = ['BMI', 'Glucose','BloodPressure', 'SkinThickness'] # 权重按距离的倒数表示。在这种情况下,查询点的近邻比远处的近邻具有更大的影响力 SkinThickness_imputer = KNNImputer(weights = 'distance') all_data[corr_SkinThickness] = SkinThickness...
KNNImputer 使用 k-Nearest-Neighbors 算法找到缺失值的最佳替代: from sklearn.impute import KNNImputer import numpy as np X = [[1, 2, np.nan], [3, 4, 3], [np.nan, 6, 5], [8, 8, 7]] imputer = KNNImputer(n_neighbors=2) ...
③用knn近邻来补缺失值 这个KNNImputer类提供了使用k-最近邻方法填充缺失值的估算。 import numpy as np from sklearn.impute import KNNImputer nan = np.nan X = [[1, 2, nan], [3, 4, 3], [nan, 6, 5], [8, 8, 7]] imputer = KNNImputer(n_neighbors=2, weights="uniform") imputer.fi...
# kNN分类器 from sklearn.neighbors import KNeighborsClassifier # kNN数据空值填充 from sklearn.impute import KNNImputer # 计算带有空值的欧式距离 from sklearn.metrics.pairwise import nan_euclidean_distances # 交叉验证 from sklearn.model_selection import cross_val_score ...
这个KNNImputer类提供了使用k-最近邻方法填充缺失值的估算。默认情况下,支持缺失值的欧氏距离度量,nan_euclidean_distances,用于查找最近的邻居。每个缺失的特性都使用n_neighbors具有该功能值的最近邻居。 fromsklearn.imputeimportKNNImputer help(KNNImputer): ...
本文简要介绍python语言中sklearn.impute.KNNImputer的用法。 用法: classsklearn.impute.KNNImputer(*, missing_values=nan, n_neighbors=5, weights='uniform', metric='nan_euclidean', copy=True, add_indicator=False) 使用k-Nearest Neighbors 完成缺失值的插补。
ImportError: 无法从 'sklearn.impute' 导入名称 'KNNImputer' (C:\Users\aura-\Anaconda3\lib\site-packages\sklearn\impute_ init _.py)我已经将sklearn更新到最新版本。(base) C:\Users\aura->pip install -U scikit-learn Requirement already up-to-date: scikit-learn in c:\users\aura-\anaconda3\...