现在我们有一组数据,户外的天气情况,温度,湿度,风。还有叶子萌芽的时间。 01 — Decision Tree - Regression 让我们用一张列表看懂这笔数据对于一组数据来说最重要的是,预测样本(Predictors),预测值(Target)…
ax.set_ylabel("score") ax.set_title("Decision Tree Regression") ax.legend(framealpha=0.5) plt.show() X_train,X_test,y_train,y_test=creat_data(200) test_DecisionTreeRegressor_depth(X_train,X_test,y_train,y_test,maxdepth=12) 由上图我们可以看出,当我们使用train_test进行数据集的分割的...
通过给定一些特征变量(如平均房间数、犯罪率等),我们的目标是预测对应房屋的房价。 # 导入所需的库和模块fromsklearn.datasetsimportload_bostonfromsklearn.model_selectionimporttrain_test_splitfromsklearn.treeimportDecisionTreeRegressorfromsklearn.metricsimportmean_absolute_error,mean_squared_errorfromsklearn.tree...
5. 创建决策树回归模型:使用sklearn库中的DecisionTreeRegressor类创建决策树回归模型,并根据需要设置相关参数。 6. 模型训练:在训练集上拟合决策树回归模型,使用fit()函数。 7. 模型预测:使用训练好的模型对测试集的输入特征进行预测,使用predict()函数。 8. 模型评价:通过比较预测结果与真实目标变量值,在测试集上...
DecisionTreeClassRegression importpandasaspd# 数据处理fromsklearn.treeimportDecisionTreeRegressor# 回归树fromsklearn.model_selectionimportcross_val_score# 交叉验证 # 导入数据df = pd.read_csv("./data//boston_house_prices.csv") df.head() # 特征值data = df.iloc[:,:-1] ...
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决策树/范例二:Multi-output Decision Tree Regression http://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression_multioutput.html 范例目的 此范例用决策树说明多输出迴归的例子,利用带有杂讯的特征及目标值模拟出近似圆的局部线性迴归。
2. Decision Tree for Regression The process of solving regression problems with decision trees usingScikit-Learnis very similar to that of classification. However for regression we use theDecisionTreeRegressorclass of the tree library. Also the evaluation metrics for regression differ from those of cl...
Decision tree for regression 1 if x2<3085.5 then node 2 elseif x2>=3085.5 then node 3 else 23.7181 2 if x1<89 then node 4 elseif x1>=89 then node 5 else 28.7931 3 if x1<115 then node 6 elseif x1>=115 then node 7 else 15.5417 4 if x2<2162 then node 8 elseif x2>=2162...
示例5: test_decision_tree_regression ▲点赞 1▼ deftest_decision_tree_regression(filename):start_time = time.time() scores = []fromsklearn.treeimportDecisionTreeRegressordf = pd.read_csv(filename) h_indep = df.columns[:-1] h_dep = df.columns[-1]for_inxrange(10):# print "- ",sy...