import matplotlib.pyplot as plt decisionNode = dict(boxstyle="sawtooth", fc="0.8") leafNode = dict(boxstyle="round4", fc="0.8") arrow_args = dict(arrowstyle="<-") def getNumLeafs(myTree): numLeafs = 0 f…

import numpy as np import operator as op from math import log def createDataSet(): dataSet = [[1, 1, 'yes'], [1, 1, 'yes'], [1, 0, 'no'], [0, 1, 'no'], [0, 1, 'no']] labels = ['no surfacing','flippers'] return dataSet, labels dataSet,labels = createD…

import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor def load_data(): ''' 加载用于分类问题的数据集.数据集采用 scikit-…

import numpy as np import matplotlib.pyplot as plt from sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier,DecisionTreeRegressor def creat_data(n): np.random.seed(0) X = 5 * np…

import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D from sklearn.model_selection import train_test_split from sklearn import datasets, linear_model,discriminant_analysis def load_data()…

import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D from sklearn import datasets, linear_model from sklearn.model_selection import train_test_split def load_data(): # 使用 scikit-learn 自带…

import numpy as np import matplotlib.pyplot as plt from matplotlib import cm from mpl_toolkits.mplot3d import Axes3D from sklearn import datasets, linear_model from sklearn.model_selection import train_test_split def load_data(): diabetes = datasets.…

import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, linear_model from sklearn.model_selection import train_test_split def load_data(): diabetes = datasets.load_diabetes() return train_test_split(diabetes.data,diabetes.tar…

import numpy as np import matplotlib.pyplot as plt from sklearn import datasets, linear_model from sklearn.model_selection import train_test_split def load_data(): diabetes = datasets.load_diabetes() return train_test_split(diabetes.data,diabetes.tar…

import numpy as np from sklearn import datasets,linear_model from sklearn.model_selection import train_test_split def load_data(): diabetes = datasets.load_diabetes() return train_test_split(diabetes.data,diabetes.target,test_size=0.25,random_state=0…