首先建立linear_regression.py文件,用于实现线性回归的类文件,包含了线性回归内部的核心函数: # -*- coding: utf-8 -*- import numpy as np class LinerRegression(object): def __init__(self, learning_rate=0.01, max_iter=100, seed=None): np.random.seed(seed) self.lr = learning_rate self.max_...
print("Cross-validation scores:", scores)超参数调整（可选）from sklearn.model_selection import GridSearchCV param_grid = {'fit_intercept': [True, False], 'normalize': [True, False]} grid_search = GridSearchCV(LinearRegression(), param_grid, cv=5, scoring='neg_mean_squared_error')grid_...
scikit-learn的cross-validation交叉验证代码: >>
有一组交叉验证方法,我来介绍其中的两个:第一个是K-Folds Cross Validation,第二个是Leave One Out Cross Validation(LOOCV)。 K-Folds 交叉验证 在K-Folds交叉验证中,我们将数据分割成k个不同的子集。我们使用第k-1个子集来训练数据,并留下最后一个子集作为测试数据。然后,我们对每个子集模型计算平均值,接下...
def linearCrossValidation(self, data, k, randomize=True): if randomize: data = list(data) shuffle(data) slices = [data[i::k] for i in range(k)] for i in range(k): validation = slices[i] train = [ data for s in slices if s is not validation for data in s ] train = np....
from sklearn.cross_validation import train_test_split 更新为下面的代码 ''' from sklearn.model_selection import train_test_split #建立训练数据和测试数据 X_train , X_test , y_train , y_test = train_test_split(exam_X , exam_y ,
fromsklearnimportlinear_model fromsklearn.cross_validationimporttrain_test_split fromsklearnimportmetrics importos importmatplotlib.pyplot as plt importpandas as pd importnumpy as np '''---load 数据集---''' dataset=datasets.load_boston() # x 训练特征:['CRIM'...
from sklearn.cross_validation import cross_val_score, cross_val_predict from sklearn import metrics 既然我们已经把数据集分成了测试集和训练集,这里我们再在原有基础上进行交叉验证,看看准确率得分变化: # Perform 6-fold cross validation scores = cross_val_score(model, df, y, cv=6) ...
cross_validation import train_test_split #把数据分类 X_train, X_test, Y_train, Y_test = train_test_split(exam_X, exam_Y, train_size = 0.8) #因为特征只有一个,所以要改变一下数据形状 X_train = X_train.values.reshape(-1, 1) X_test = X_test.values.reshape(-1, 1) #导入 from ...
我们将通过 K 次交叉验证来预估得到的学习模型在未知数据上的表现。这就意味着我们将创建并评估 K 个模型并预估这 K 个模型的平均误差。辅助函数 cross_validation_split()、rmse_metric() 和 evaluate_algorithm() 用于求导根均方差以及评估每一个生成的模型。