X_train,X_test,y_train,y_test=train_test_split(X,y)#用信息增益启发式算法建立决策树pipeline=Pipeline([('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...
用法: classsklearn.tree.DecisionTreeClassifier(*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=...
代码实例 from sklearn.feature_extraction import DictVectorizer from sklearn import preprocessing from sklearn import tree import csv AllElectronics=open('D:\daacheng\Python\PythonCode\machineLearning\AllElectronics.csv','rt')#打开csv文件 readers=csv.reader(AllElectronics) headers=next(readers)#表头 ['RI...
pythonCopy codefrom sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score # 加载数据集 iris = datasets.load_iris() X = iris.data y = iris.target # 划分数据集为训练集和测试集 X_t...
The first is representing the decision tree modelas a function. from sklearn.tree import _tree def tree_to_code(tree, feature_names): """ Outputs a decision tree model as a Python function Parameters: --- tree: decision tree model The decision ...
#from sklearn.tree import DecisionTreeClassifier# Step 2: Make an instance of the Modelclf = DecisionTreeClassifier(max_depth = 2, random_state = 0)# Step 3: Train the model on the dataclf.fit(X_train, Y_train)# Step 4: Predict labels of unseen (test) data# Not doing this st...
Run code Decision Tree Classifier Building in Scikit-learn Importing Required Libraries Let's first load the required libraries. # Load libraries import pandas as pd from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier from sklearn.model_selection import train_test_split ...
Run code Decision Tree Classifier Building in Scikit-learn Importing Required Libraries Let's first load the required libraries. # Load libraries import pandas as pd from sklearn.tree import DecisionTreeClassifier # Import Decision Tree Classifier from sklearn.model_selection import train_test_split ...
下面是一个使用Python的示例代码来构建和使用决策树的例子: pythonCopy codefrom sklearn import datasets from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeClassifier from sklearn.metrics import accuracy_score
以下是一个使用Python的scikit-learn库实现GBDT的代码示例: AI检测代码解析 pythonCopy codefrom sklearn.ensemble import GradientBoostingClassifier from sklearn.datasets import make_classification from sklearn.model_selection import train_test_split from sklearn.metrics import accuracy_score ...