In [16]: wine_data=data.iloc[:-5,:] wine_target=data.iloc[-5:,:] In [17]: from sklearn.tree import DecisionTreeClassifier from sklearn.model_selection import train_test_split from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split x=wine_data.iloc[:,1:].values y=wine_data.iloc[:,0].values x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=42) #建立模型 dtc=DecisionTreeClassifier(criterion='entropy')#基于熵评价纯度 dtc.fit(x_train,y_train)#拟合数据 y_pre=dtc.predict(x_test) y_pre Out[17]: array([3.0, 1.0, 3.0, 2.0, 2.0, 2.0, 2.0, 1.0, 3.0, 2.0, 3.0, 1.0, 2.0, 3.0, 2.0, 1.0, 2.0, 1.0, 3.0, 2.0, 2.0, 2.0, 2.0, 1.0, 3.0, 2.0, 3.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 2.0, 3.0, 3.0, 2.0, 1.0, 1.0, 1.0, 1.0, 1.0, 3.0, 1.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 3.0, 3.0]) In [18]: dtc.predict(wine_target.iloc[:,1:].values) Out[18]: array([2.0, 2.0, 2.0, 3.0, 1.0]) In [19]: from sklearn.metrics import mean_squared_error #先获得预测的y值y_pre y_pre=dtc.predict(x_test) mean_squared_error(y_test,y_pre) Out[19]: 0.0 In [20]: print("决策树 训练精度：",dtc.score(x_test,y_test)) print("决策树 泛化精度：",dtc.score(x_train,y_train)) 决策树 训练精度： 1.0 决策树 泛化精度： 1.0 In [21]: #KNN最近邻分类算法 from sklearn.neighbors import KNeighborsClassifier from sklearn.datasets import load_digits from sklearn.model_selection import train_test_split x=wine_data.iloc[:,1:].values y=wine_data.iloc[:,0].values x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=125) dtr=KNeighborsClassifier() dtr.fit(x_train,y_train) dtr.score(x_test,y_test) Out[21]: 0.9807692307692307 In [22]: model_knn=KNeighborsClassifier(n_neighbors=5)#看5个近邻的类别确定分类 model_knn.fit(x_train,y_train) #预测 model_knn.predict(x_test) Out[22]: array([3.0, 3.0, 1.0, 2.0, 1.0, 3.0, 3.0, 1.0, 2.0, 3.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 2.0, 3.0, 1.0, 1.0, 3.0, 1.0, 2.0, 1.0, 2.0, 3.0, 3.0, 2.0, 2.0, 1.0, 1.0, 2.0, 1.0, 1.0, 2.0, 3.0, 1.0, 3.0, 3.0, 2.0, 2.0, 2.0, 2.0, 1.0, 1.0, 1.0, 1.0, 2.0, 3.0, 2.0, 1.0]) In [23]: dtr.predict(wine_target.iloc[:,1:].values) Out[23]: array([2.0, 2.0, 3.0, 3.0, 1.0]) In [24]: neighbors = 3 from sklearn.neighbors import KNeighborsClassifier knn = KNeighborsClassifier(neighbors) knn.fit(x_train,y_train) print("KNN 训练精度：",knn.score(x_test,y_test)) print("KNN 泛化精度：",knn.score(x_train,y_train)) KNN 训练精度： 0.9615384615384616 KNN 泛化精度： 0.9586776859504132代码解释

这段代码主要是进行机器学习的分类任务，使用了决策树和K近邻算法。首先将数据分为训练集和测试集，然后使用DecisionTreeClassifier和KNeighborsClassifier分别建立决策树和K近邻分类模型，并使用训练数据拟合模型。决策树模型使用熵评价纯度。使用模型对训练集和测试集进行评估，计算预测准确率。最后使用模型对新的数据进行预测。使用mean_squared_error计算决策树模型的均方误差。K近邻算法中，n_neighbors表示选择的最近邻个数。最后输出模型的训练精度和泛化精度。