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 a
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) df_for_testing_scaled=scaler.transform(df_for_testing)...
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_...
To Reproduce Steps to reproduce the behavior: Train model, the issue is not related to a specific dataset See error from cesium import featurize File "/home/zoran/MyProjects/lightwood/l/lib/python3.7/site-packages/cesium-0.9.9-py3.7-linux-x86_64.egg/cesium/featurize.py", line 10, in ...
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)df_for_testing_scaled=scaler.transform(df_for_testing)df...
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) ...