y = y.values.ravel() X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25, random_state=42, stratify=y) scaler = MinMaxScaler() X_train = scaler.fit_transform(X_train) X_test = scaler.transform(X_test) 下一步是我们将使用Se...
# 归一化数据scaler = StandardScaler()X_train_scaled = scaler.fit_transform(X_train)X_test_scaled = scaler.transform(X_test) # 获取归一化后的混淆矩阵cm_with_norm = get_confusion_matrix(X_train_scaled, X_test_scaled, ...
mean_absolute_errorfromsklearn.preprocessingimportStandardScalerimportnumpyasnp# scaler = StandardScaler()# X_scaled = scaler.fit_transform(X_1)# y_scaled = scaler.fit_transform(y_1.reshape(-1, 1)
scaler = StandardScaler(copy=True, with_mean=True, with_std=True) X_train_scaled = scaler.fit_transform(X_train) X_test_scaled = scaler.transform(X_test) # 训练决策树分类器 clf = DecisionTreeClassifier(random_state=42) clf.fit(X_train_scaled, y_train) # 预测测试集 y_pred = clf.pre...
scaler.fit(X_train) X_train_s = scaler.transform(X_train) X_test_s = scaler.transform(X_test) print('训练数据形状:') print(X_train_s.shape,y_train.shape) print('测试数据形状:') print(X_test_s.shape,y_test.shape) 自定义ELM类 ...
X_test_scaled = scaler.transform(X_test) # 4. 初始化Ridge回归模型并设置参数 ridge_reg = Ridge(alpha=1.0, fit_intercept=True, solver='auto', random_state=42) # 5. 训练模型 ridge_reg.fit(X_train_scaled, y_train) # 6. 评估模型性能 ...
print('\n标准化 X 转换后:scaler_transform_X\n',scaler.transform(X) ) print('\n标准化 X 转换后均值:mean=',scaler.transform(X).mean(axis= 0 )) print('标准化 X 转换后标准差:std=',scaler.transform(X).std(axis= 0 )) #可以直接使用训练集对测试集数据[[ - 1. , 1. , 0. ]]进...
scaler = StandardScaler() X = scaler.fit_transform(np.array(data.iloc[:, :-1], dtype = float)) 将数据分为训练集和测试集 In [0]: X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2) In [12]:
['Feature1', 'Feature2', 'Feature3', 'Feature4', 'Feature5'])# 数据标准化scaler = StandardScaler()df_scaled = scaler.fit_transform(df)# 初始化PCA对象pca = PCA()# 拟合并转换数据principal_components = pca.fit_transform(df_scaled)# 打印主成分数量和解释的方差比explained_variance = pca....
使用fit_transform()函数对数据进行缩放处理: 代码语言:txt 复制 scaled_data = scaler.fit_transform(data) 缩放后的数据将存储在scaled_data变量中,可以进行进一步的处理或使用。 StandardScaler()的优势包括: 适用于连续型数据,可以处理单行浮点数或多行数据。 缩放后的数据符合标准正态分布,有利于某些机器学习...