def cv_lgm(num_leaves,max_depth,lambda_l1,lambda_l2,bagging_fraction,bagging_freq,colsample_bytree): kf = StratifiedKFold(n_splits = 5, shuffle = True, random_state = 0) # kf = KFold(n_splits = 5, shuffle = True, random_state = 0) y_pred = np.zeros(len(x_test)) for fol...
python DecisionTreeClassifier 参数 前几天学习了一下python的turtle库,它是python中一个绘制图像的函数库,用海龟可以画出各种图像,学习之后我画了可爱的小黄人,和太阳等图案,觉得很好玩很有趣,在这里想介绍一下turtle的使用详解,感兴趣或者需要的朋友可以参考一下。 首先说明一下turtle绘图的基础知识: 1. 画布(canv...
depths =range(2,15)# 设置参数字典param_grid = {'max_depth': depths,'min_impurity_decrease': values}# 初始化的分类器、参数取值,交叉验证的次数(cv=5,即数据划分5份)model = GridSearchCV(DecisionTreeClassifier(), param_grid, cv=5)# 直接把全部数据进行训练model.fit(data_train, data_target) ...
clf = DecisionTreeClassifier(max_depth=4) #拟合模型 clf = clf.fit(X_train, y_train) score = clf.score(X_test,y_test) print(score) #测试结果 # 混淆矩阵 from sklearn.metrics import confusion_matrix test_predict = clf.predict(X_test) cm = confusion_matrix(y_test,test_predict) print(...
from sklearn importtree#Assumed you have, X (predictor) and Y (target) for training data set and x_test(predictor) of test_dataset #Create tree objectmodel=tree.DecisionTreeClassifier(criterion='gini')#for classification, here you can change the algorithm as gini or entropy (information gain...
"决策树": DecisionTreeClassifier(random_state=42), "随机森林": RandomForestClassifier(random_state=42), "逻辑回归": LogisticRegression(random_state=42) } # 训练模型并计算准确率 accuracies = {} for name, model in models.items():
python decisiontreeclassifier使用 `DecisionTreeClassifier`是Python中`sklearn`库中的一个分类器模块,它基于决策树算法进行分类。以下是使用`DecisionTreeClassifier`的一些步骤: 1.导入所需库: ```python from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn...
Let's create a decision tree model using Scikit-learn. # Create Decision Tree classifer object clf = DecisionTreeClassifier() # Train Decision Tree Classifer clf = clf.fit(X_train,y_train) #Predict the response for test dataset y_pred = clf.predict(X_test) Run code Powered By Evalua...
python机器学习之decisiontreeclassifier python机器学习之decisiontreeclassifier #决策树算法的原理是⼀系列if_else的逻辑迭代。适⽤于对数据进⾏分类和回归，优点是对于数据的本⾝要求不⾼，直观容易理解，缺点是容易过拟合和泛化能⼒不强。对于回归⽽⾔，不能外推。from sklearn.tree import DecisionTree...
(parent_connection_label,node)returnnode=self.Node(best_col)parent_node.connect(parent_connection_label,node)new_columns=[colforcolincolumnsifcol!=best_col]# Recursively constructing decision treesforsplited_value,splited_datainmax_splited.items():self.construct(node,splited_value,splited_data,new_...