the structure of this post will mimic the way the Decision Tree Class is defined in sklearn. Since sklearn is built upon CART, we will mainly talk about CART. In the end we will have a comparison
from sklearn import tree from sklearn import model_selection from sklearn.datasets import load_iris from sklearn.grid_search import GridSearchCV from sklearn.metrics import confusion_matrix from sklearn.metrics import precision_score from sklearn.metrics import recall_score from sklearn.metrics impor...
sklearn.tree._tree.Tree def __cinit__(self, int n_features, np.ndarray[SIZE_t, ndim=1] n_classes, int n_outputs): 1. 2. 特征数 类别数 label维度 # Use BestFirst if max_leaf_nodes given; use DepthFirst otherwise if max_leaf_nodes < 0: builder = DepthFirstTreeBuilder(splitter, ...
print(__doc__)importnumpyasnpimportmatplotlib.pyplotaspltfromsklearn.datasetsimportload_irisfromsklearn.treeimportDecisionTreeClassifier, plot_tree# Parametersn_classes =3plot_colors ="ryb"plot_step =0.02# Load datairis = load_iris()forpairidx, pairinenumerate([[0,1], [0,2], [0,3], [1...
import pandasaspdfromsklearn.tree import DecisionTreeClassifierfromsklearn.cross_validation import train_test_splitfromsklearn.metrics import classification_reportfromsklearn.pipeline import Pipelinefromsklearn.grid_search import GridSearchCV importzipfile#压缩节省空间z=zipfile.ZipFile('ad-dataset.zip')#...
初创是在Google Summer of Code的暑期活动中。sklearn用Numpy作线性代数和array操作,用Cython提高performance. Sklearn对Pandas 的dataframe和Scipy也有很好的兼容。sklearn在机器学习的应用主要有分类(classification),回归(regression)和聚类(clustering).决策树(decision tree)是众多机器学习算法的一种,树的深度越大,学习...
# 2. 构建决策树myDecisionTree = createTreeNode(dataSet,labels,[]) # 3. 输出print(myDecisionTree) 输出: {'F3-HOME': {0: {'F2-WORK': {0: 'no', 1: 'yes'}}, 1: 'yes'}} 十、SkLearn库实现决策树并可视化 10.1 Graphviz可视化库安装 ...
(X, y, test_size=0.3, random_state=42) # 创建决策树分类器 clf = DecisionTreeClassifier() # 在训练集上训练模型 clf.fit(X_train, y_train) # 在测试集上评估模型 accuracy = clf.score(X_test, y_test) print("Accuracy:", accuracy) from sklearn.tree import export_graphviz import graphviz...
from sklearn.tree import DecisionTreeClassifier # 加载数据 iris = load_iris()X = iris.data y = iris.target # 创建并训练模型 clf = DecisionTreeClassifier(max_depth=3, random_state=42)clf.fit(X, y)2. 模型可视化 决策树的可视化有助于理解模型的决策逻辑。可以使用graphviz库配合scikit-learn的...
决策树(Decision Tree)SkLearn 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58