下面以load_digits手写数字数据集,举例说明train_test_split的几种不同用法,可以按照需求使用。 数据导入和处理 from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split from sklearn.svm importSVCfrom sklearn.metrics import accuracy_score # 加载Digits数据集 digits = l...
from sklearn import datasets digits = datasets.load_digits() # 加载数字样本 X = digits.data # 特征数据 y = digits.target # 标签 print(X.shape) print(X[0]) print(np.array(X[0]).reshape(8, 8)) # 训练数据都是1d的,转成8x8的2d矩阵后,能看出数字的轮廓 print("第一条数据的标签是:"...
>>> from sklearn.datasets import load_digits >>> from sklearn.feature_selection import SelectPercentile, chi2 >>> X, y = load_digits(return_X_y=True) >>> X.shape (1797, 64) >>> X_new = SelectPercentile(chi2, percentile=10).fit_transform(X, y) >>> X_new.shape (1797, 7) 1...
importumap from sklearn.datasetsimportload_digits from sklearn.datasetsimportmake_moonsimportmatplotlib.pyplotasplt # 手写数字数据集 digits=load_digits()data=digits.data target=digits.target # 使用UMAP进行降维 reducer=umap.UMAP()embedding=reducer.fit_transform(data)# 可视化降维后的数据 plt.scatter(emb...
fromsklearnimportdatasetsfromsklearn.datasetsimportload_digits 数据集 Scikit-learn的数据集子模块datasets提供了两类数据集:一类是模块内置的小型数据集,这类数据集有助于理解和演示机器学习模型或算法,但由于数据规模较小,无法代表真实世界的机器学习任务;另一类是需要从外部数据源下载的数据集,这类数据集规模都比较...
load_digits() X = digits.data y = digits.target X变量包含了8x8像素的手写数字图像,y变量包含了每个图像对应的标签(0到9的数字)。接下来,我们将使用train_test_split函数将数据集划分为训练集和测试集。 from sklearn.model_selection import train_test_split # 划分训练集和测试集 X_train, X_test, y...
digits=load_digits()X_train,X_test,y_train,y_test=train_test_split(digits.data,digits.target,train_size=0.75,test_size=0.25,random_state=42)tpot=TPOTClassifier(generations=5,population_size=50,verbosity=2,random_state=42)tpot.fit(X_train,y_train)print(tpot.score(X_test,y_test))tpot...
fromsklearn.datasets import load_digits digits = load_digits() plt.matshow(digits.images[0]) plt.show() 1.2 创建数据集 我们除了可以使用sklearn自带的数据集,还可以自己去创建训练样本, 具体用法可以参考:https://scikit-learn.org/stable/datasets/ ...
load_digits()#读取数据 X = digits.data#定义X y = digits.target#定义y In [ ]: from sklearn.model_selection import train_test_split #载入数据切分工具 X_train, X_test, y_train, y_test = train_test_split(X,y,test_size = 0.2)#数据切分 In [ ]: %time from sklearn.neighbors ...
from tpot import TPOTClassifierfrom sklearn.datasets import load_digitsfrom sklearn.model_selection import train_test_splitdigits = load_digits()X_train, X_test, y_train, y_test = train_test_split(digits.data,digits.target,train_size=0.75,test_...