python3 学习机器学习api 使用了三种集成回归模型 git: https://github.com/linyi0604/MachineLearning 代码: from sklearn.datasets import load_boston from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.ensemble
python3学习使用api 线性回归,和 随机参数回归 git: https://github.com/linyi0604/MachineLearning from sklearn.datasets import load_boston from sklearn.cross_validation import train_test_split from sklearn.preprocessing import StandardScaler from sklearn.linear_model i
import numpy as np import pandas as pd from Udacity.model_check.boston_house_price import visuals as vs # Supplementary code from sklearn.model_selection import ShuffleSplit # Pretty display for notebooks # 让结果在notebook中显示 # Load the Boston housing d
import numpy as np import matplotlib as mpl mpl.rcParams["font.sans-serif"] = ["SimHei"] import matplotlib.pyplot as plt import pandas as pd from sklearn.model_selection import train_test_split from sklearn.metrics import mean_squared_
# -*- coding: utf-8 -*- """ Created on Sat Oct 20 14:03:05 2018 @author: 12958 """ import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns # 忽略警告 import warnings warnings.filterwarnings('i
