当训练集传入Scaler中,这个Scaler也有一个fit,这个fit算法就是求出训练数据集对应的一些统计指标,比如,对于均值方差归一化来说,fit操作之后,就求出了训练集的均值和方差,之后Scaler中保存了关键的信息,如果再来其他样例之后,Scaler就可以非常简单的对输入样例进行transform得到相应的输出结果。 其实对比机器学习算法,只是...
self.mean_ =Noneself.scale_ =None;deffit(self, X):assertX.ndim ==2,"The dimension of X must be 2"self.mean_ = np.array([np.mean(X[:, i])foriinrange(X.shape[1])]) self.scale_ = np.array([np.std(X[:, i])foriinrange(X.shape[1])])returnselfdeftranform(self, X):a...
X_scaled = scaler.fit_transform(X)# 打印前5个样本的标准化后数据print("\n标准化后的数据(前5个样本):")print(pd.DataFrame(X_scaled, columns=iris.feature_names).head())# 3. 分割数据集X_train, X_test, y_train, y_te...
fromsklearn.model_selectionimporttrain_test_split X_train, X_test, y_train, y_test= train_test_split(X, y, test_size=0.2, random_state=666) fromsklearn.preprocessingimportStandardScaler standardscaler=StandardScaler() standardscaler.fit(X_train) StandardScaler(copy=True, with_mean=True, with_std...
X = scaler.fit_transform(X)划分训练集和测试集 X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)实例化逻辑回归模型 model = LogisticRegression()训练模型 model.fit(X_train, y_train)预测 predictions = model.predict(X_test)评估模型 accuracy = ...
scaler = preprocessing.StandardScaler().fit(X) print(scaler.transform(X)) 众多的机器学习算法 Scikit-learn提供了各种常用的监督学习和无监督学习算法,包括回归、分类、聚类、降维等。这些算法的API设计统一且一致,使得在不同的算法间切换变得非常简单。
数据标准化是指将数据按比例缩放，使之落入一个小的特定区间。常用的方法是Z-score标准化，将数据转换为均值为0、标准差为1的分布。from sklearn.preprocessing import StandardScalerimport numpy as np# 示例数据data = np.array([[1, 2], [2, 3], [3, 4], [4, 5]])# 创建StandardScaler对象scaler ...
数据归一化 scikit-learn中的Scaler 83327712 2020-07-30 关注 关注 import numpy as np from sklearn import datasets # 获取数据 iris = datasets.load_iris() X = iris.data y = iris.target # 数据分割 from sklearn.model_selection import train_test_split X_train, X_test, y_train, y_test =...
import StandardScalerfrom sklearn.model_selection import train_test_split# 加载数据集data = load_breast_cancer()# 划分训练集和测试集X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=)# 创建流水线pipeline = Pipeline([ ('scaler'...
>>>from sklearnimportpreprocessing>>>importnumpyasnp>>>x=np.array([1,-2,3,-4,5,6]).reshape(-1,1)>>>xarray([[1],[-2],[3],[-4],[5],[6]])>>>scaler=preprocessing.StandardScaler().fit(x)>>>x_scaled=scaler.transform(x)>>>x_scaledarray([[-0.13912167],[-0.97385168],[0.4173...