weixin_44611266的博客

##逻辑回归 import numpy as np import pandas as pd from sklearn.datasets import load_iris from sklearn.linear_model import LogisticRegression #1.导入数据 #data = pd.read_csv(’’) #2.数据预处理 #略，最终生成x_train,y_train...

##Adaboost-分类 from sklearn.ensemble import AdaBoostClassifier from sklearn.datasets import make_classification #1.导入数据 #data = pd.read_csv(’’) #2.数据预处理 #略，最终生成x_train,y_train,x_test #导入sklearn的数据集 x_t...

##线性回归 import numpy as np import pandas as pd from sklearn.linear_model import LinearRegression #1.导入数据 #data = pd.read_csv(’’) #2.数据预处理 #略，最终生成x_train,y_train,x_test #此处用随机生成数据 #数据量m m = 20 #特征数n n =...

##朴素贝叶斯 import numpy as np import pandas as pd from sklearn.datasets import load_iris from sklearn.naive_bayes import GaussianNB #1.导入数据 #data = pd.read_csv(’’) #2.数据预处理 #略，最终生成x_train,y_train,x_test ...

##K-means import numpy as np import pandas as pd from sklearn.cluster import KMeans #1.导入数据 #data = pd.read_csv(’’) #2.数据预处理 #略，最终生成x_train,x_test x_train = np.array([[1, 2, 3], [1, 4, 6], [1, 0, 9...

##DBSCAN import numpy as np import pandas as pd from sklearn.cluster import DBSCAN #1.导入数据 #data = pd.read_csv(’’) #2.数据预处理 #略，最终生成x_train,x_test x_train = np.array([[1, 2, 3], [1, 4, 6], [1, 0, 9], [...

##随机森林-分类 from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import make_classification #1.导入数据 #data = pd.read_csv(’’) #2.数据预处理 #略，最终生成x_train,y_train,x_test #导入sklearn的数据集 x_t...