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基本绘图，设置子图间距，设置折线类型，标签，设置坐标轴范围、标签，设置子图标题 import matplotlib.pyplot as plt import numpy as np from sklearn.linear_model import LinearRegression x1,y1=[-1,0,1],[0,0,1] x2,y2=np.arange(5),np.random.rand(5,1) x3,n_bins=np.random.randn(200,1),50 x4,y4=np.random

numpy.random.rand(d0, d1, ..., dn) 返回给定形状的数组，其元素来自[0,1)均匀分布。 numpy.random.randint(low, high=None, size=None, dtype=int) 返回来自[low,high)均匀分布的整数。如果没有给出，则[0,low) numpy.random.randn(d0, d1, ..., dn)返回给定形状的数组，其元素来自标准分布。 numpy.random.random(size=None)返回 [0.0.

Chapter 3 Classfication 主要讲分类问题的性能度量选择。 SGDClassfier 适合处理大数据集和在线学习This classifier has the advantage of being capable of handling very large datasets efficiently. This is in part because SGD deals with training instances independently, one at a time (which

Chapter 2 End to End Machine Learning Project 对于类别特征使用OrdinalEncoder，ML算法会认为离得近的数字特征比离得远的数字特征相似。所以推荐OneHotEncoder。 If a categorical attribute has a large number of possible categories (e.g., country code, profession, species, etc.), then one-hot encod‐