我正在尝试在 Python 中加载 MNIST 原始数据集。 sklearn.datasets.fetch_openml 函数似乎对此不起作用。
import statsmodels.api as sm from sklearn.datasets import fetch_openml import pandas as pd # Load the Boston housing dataset from OpenML boston = fetch_openml(name='boston', version=1) boston_df = pd.DataFrame(boston.data, columns=boston.feature_names) boston_df['PRICE'] = boston.target...
from sklearn.datasets import fetch_openml from sklearn.svm import SVC mnist = fetch_openml('mnist_784', version=1, as_frame=False) # 默认返回Pandas的DF类型 # sklearn加载的数据集类似字典结构 from sklearn.preprocessing import StandardScaler X, y = mnist["data"], mnist["target"] stder =...
from sklearn.datasets import fetch_openml from sklearn.preprocessing import RobustScaler import matplotlib.pyplot as plt import seaborn as sns from pyod.models.iforest import IForest from pyod.models.lof import LOF from pyod.models.ecod import ECOD # Collect the data data = fetch_openml('abalo...
importnumpyasnpimportmatplotlib.pyplotaspltfromsklearn.datasetsimportfetch_openmlfromsklearn.model_selectionimporttrain_test_splitfromsklearn.preprocessingimportStandardScaler# 加载数据集mnist=fetch_openml('mnist_784')X,y=mnist['data'],mnist['target']# 数据分割X_train,X_test,y_train,y_test=train_...
from sklearn.datasets import fetch_openml mnist = fetch_openml('mnist_784') mnist 1. 2. 3. X,y = mnist['data'], mnist['target'] X.shape # 输出 (70000, 784) # (70000 数据集中共70000个样本 每个样本为28*28大小 即 784)
importstatsmodels.apiassmfromsklearn.datasetsimportfetch_openmlimportpandasaspd # Load the Boston housing dataset from OpenMLboston = fetch_openml(name='boston', version=1)boston_df = pd.DataFrame(boston.data, columns=boston.feature_names)boston_df['PRICE'] = boston.target ...
OneHotEncoder from sklearn.compose import make_column_selector as selector from sklearn.pipeline import Pipeline from raiwidgets import FairnessDashboard # Load the census dataset data = fetch_openml(data_id=1590, as_frame=True) X_raw = data.data y = (data.target == ">50K") * 1 # ...
openml UCI 机器学习库 Kaggle SciKit-Learn库中,也有自带一些数据集可以尝试加载。 datasets模块中也包含了获取其他流行数据集的方法,例如datasets.fetch_openml可以从openml存储库获取数据集。 代码: def get_data(): # 通过名称或数据集ID从openml获取数据集 from sklearn.datasets import fetch_openml # Mnist...
import numpy as np import matplotlib import matplotlib.pyplot as plt from sklearn.datasets import fetch_openml 导入数据集 mnist = fetch_openml('mnist_784') 导入数据X,y X = mnist['data'] y = mnist['target'] 这个数据集帮你分好类了,不用进行train_test_split的分割 X_train = np...