python import pandas as pd from sklearn.model_selection import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model import LogisticRegression from sklearn.metrics import accuracy_score # 假设我们有一个CSV文件包含数据集 df = pd.read_csv('dataset.csv') # 数据...
(也可以使用StandardScaler) scaler = MinMaxScaler(feature_range=(0,1))df_for_training_scaled = scaler.fit_transform(df_for_training)df_for_testing_scaled=scaler.transform(df_for_testing)df_for_training_scaled 将数据拆分为X和Y,这是最重要的部分,正确阅读每一个步骤。 defcreateXY(dataset,n_past):...
'StandardScaler', 'add_dummy_feature', 'PolynomialFeatures', 'binarize', 'normalize', 'scale', 'robust_scale', 'maxabs_scale', 'minmax_scale', 'label_binarize', 'quantile_transform', 'power_transform', 'Imputer' ] Sign up for freeto join this conversation on GitHub. Already have an acc...
如果设为了False,而你又想标准化特征集合,则需要使用 sklearn.preprocessing.StandardScaler类来进行预处理。 copy_X: 布尔类型,初始化为True。True则,特征集合不变,反之会被复写。 n_jobs: The number of jobs to use for the computation 初始为None,表示用1个处理器计算;-1代表所有处理器,只用于多个目标集...
import StandardScaler from .data import QuantileTransformer from .data import add_dummy_feature from .data import binarize from .data import normalize from .data import scale from .data import robust_scale from .data import maxabs_scale from .data import minmax_scale from .data import quantile_...
from sklearn.preprocessing import StandardScalerfrom sklearn.decomposition import PCAwine = pd.read_csv('C:/Users/hasee/Desktop/python数据分析与应用/第6章/03-实训数据/wine.csv', sep=',')wineq =pd.read_csv('C:/Users/hasee/Desktop/python数据分析与应用/第6章/03-实训数据/winequality.csv', ...
可以注意到数据范围非常大,并且它们没有在相同的范围内缩放,因此为了避免预测错误,让我们先使用MinMaxScaler缩放数据。(也可以使用StandardScaler) scaler = MinMaxScaler(feature_range=(0,1)) df_for_training_scaled = scaler.fit_transform(df_for_training) ...
可以注意到数据范围非常大,并且它们没有在相同的范围内缩放,因此为了避免预测错误,让我们先使用MinMaxScaler缩放数据。(也可以使用StandardScaler) scaler = MinMaxScaler(feature_range=(0,1)) df_for_training_scaled = scaler.fit_transform(df_for_training) ...