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KNNImputer class sklearn.impute.KNNImputer(*, missing_values=nan, n_neighbors=5, weights='uniform', metric='nan_euclidean', copy=True, add_indicator=False) from sklearn.impute import KNNImputer KNNImputer 파라미터 missing_values = {int, float, str, np.nan, None}, default=np.nan n_neighbors = int, default=5 weights = {‘uniform’, ‘distance’} or callable, default=’uniform’ # 예측에 사용되는 가중치 함수입니다. 가능한 값: ..

MissingIndicator class sklearn.impute.MissingIndicator(*, missing_values=nan, features='missing-only', sparse='auto', error_on_new=True) from sklearn.impute import MissingIndicator MissingIndicator 파라미터 missing_values = {int, float, str, np.nan, None}, default=np.nan features = {‘missing-only’, ‘all’}, default=’missing-only’ sparse = bool or ‘auto’, default=’auto’ 'auto'(기본값) 경우, imputer 마스크는 입력..

IterativeImputer(회귀대치) class sklearn.impute.IterativeImputer(estimator=None, *, missing_values=nan, sample_posterior=False, max_iter=10, tol=0.001, n_nearest_features=None, initial_strategy='mean', imputation_order='ascending', skip_complete=False, min_value=- inf, max_value=inf, verbose=0, random_state=None, add_indicator=False) from sklearn.experimental import enable_iterative_imputer from skl..

SimpleImputer class sklearn.impute.SimpleImputer(*, missing_values=nan, strategy='mean', fill_value=None, verbose=0, copy=True, add_indicator=False) from sklearn.impute import SimpleImputer SimpleImputer 파라미터 missing_values = {int, float, str, np.nan or None}, default=np.nan strategy = str, default=’mean’ # mean, median, constant : 숫자형 # most_frequent : 숫자형, 범주형 fill_value = {str, numerical valu..