parameters = {"criterion":("gini","entropy"), "spliter":("best","random"), "max_depth": [*range(1,10)], "min_samples_leaf" = [*range(1,50,5)], "min_impurity_decrease" = np.linspace(0,0.5,50) } clf = DecisionTreeClassifier(random = 25) GS = GridSearchCV(clf, parameters...
clf = DecisionTreeClassifier(random = 25) GS = GridSearchCV(clf, parameters, cv = 10) GS = GS.fit(Xtrain, Ytrain) GS.best_params_#最佳参数组合 GS.best_score_#最佳结果r方 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 可视化 生成可视化图,在这里仅仅只能输出一个dot文件。 dot_data = exp...
DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=160, max_features=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=3, min_weight_fraction_leaf=0.0, presort=False, random_state=None, splitter='best'))]} for param_name in sorted(parameters.keys()):...
from sklearn.tree import DecisionTreeClassifier from sklearn.tree import plot_tree # 加载鸢尾花数据集 iris = load_iris() # Parameters n_classes = 3 plot_colors = "ryb" plot_step = 0.02 # 遍历不同特征对的组合 for pairidx, pair in enumerate([[0, 1], [0, 2], [0, 3], [1, 2...
决策树(Decision Tree): 通过递归地选择最佳特征并对特征进行分割,构建树形结构进行分类。 易于理解和解释,能处理数值型和类别型数据。 可用于银行决定是否给客户贷款等场景。 支持向量机(Support Vector Machine, SVM): SVM通过寻找最大边际超平面来分隔不同的类别。
决策树回归(Decision Tree Regression): 决策树回归使用树形结构来表示输入特征与输出值之间的关系。 通过递归地将数据划分为不同的子集,并基于某些准则(如信息增益)选择最佳划分点。 决策树易于理解和解释,但可能容易过拟合。 随机森林回归(Random Forest Regression): ...
parameters={'criterion':("gini","entropy"),"max_depth":[*range(1,5)]#前面我们知道这个应该是2,所以我们给定范围1-5,'min_samples_split':[*range(5,40,5)]#最小分支节点以步长为5,在5-39循环}#定义我们要找的参数clf=tree.DecisionTreeClassifier()GS=GridSearchCV(clf,parameters,cv=10)#cv=10...
('clf',DecisionTreeClassifier(criterion='entropy')) ]) parameters = { 'clf__max_depth': (150, 155, 160), 'clf__min_samples_split': (1, 2, 3), 'clf__min_samples_leaf': (1, 2, 3) } grid_search = GridSearchCV(pipeline, parameters, n_jobs=-1,verbose=1, scoring='f1') ...
("best","random"),"max_depth":[*range(1,10)],"min_samples_leaf":[*range(1,50,5)],"min_impurity_decrease":[*np.linspace(0,0.5,20)]}clf=DecisionTreeClassifier(random_state=25)GS=GridSearchCV(clf,parameters,cv=10)GS=GS.fit(Xtrain,Ytrain)#返回最佳组合print(GS.best_params_)#返回...
parameters = {'splitter':('best','random') ,'criterion':("gini","entropy") ,"max_depth":[*range(1,10)] ,'min_samples_leaf':[*range(1,50,5)] ,'min_impurity_decrease':[*np.linspace(0,0.5,20)] } clf = tree.DecisionTreeClassifier(random_state=25) ...