清丰网站建设公司,馆陶网站建设,厦门建网站网址,河北黄骅市简介这是我的第314篇原创文章。 一、引言 对于表格数据#xff0c;一套完整的机器学习建模流程如下#xff1a; 针对不同的数据集#xff0c;有些步骤不适用#xff0c;其中橘红色框为必要步骤#xff0c;欢迎大家关注翻看我之前的一些相关文章。前面我介绍了机器学习模型的二…这是我的第314篇原创文章。 一、引言 对于表格数据一套完整的机器学习建模流程如下 针对不同的数据集有些步骤不适用其中橘红色框为必要步骤欢迎大家关注翻看我之前的一些相关文章。前面我介绍了机器学习模型的二分类任务和回归任务接下来做一下机器学习的多分类系列由于本系列案例数据质量较高有些步骤跳过了跳过的步骤将单独出文章总结在Python中可以使用Scikit-learn库来构建决策树分类模型进行多分类预测本文以预测小麦品种为例对这个过程做一个简要解读。 二、实现过程 2.1 准备数据 data pd.read_csv(rdata.csv) df pd.DataFrame(data) print(df.head()) df 2.2 提取目标变量 target Type features df.columns.drop(target) print(data[Type].value_counts()) # 顺便查看一下样本是否平衡 2.3 划分数据集 # df shuffle(df) X_train, X_test, y_train, y_test train_test_split(df[features], df[target], test_size0.2, random_state0) 2.4 归一化 # 此步可不做处理 2.5 模型的构建 model DecisionTreeClassifier(max_depth5) 2.6 模型的训练 model.fit(X_train, y_train) 2.7 模型的推理 y_pred model.predict(X_test) y_scores model.predict_proba(X_test) print(y_pred) 2.8 模型的评价 acc accuracy_score(y_test, y_pred) # 准确率acc print(facc: \n{acc}) cm confusion_matrix(y_test, y_pred) # 混淆矩阵 print(fcm: \n{cm}) cr classification_report(y_test, y_pred) # 分类报告 print(fcr: \n{cr}) 结果 print(----------------------------- precision精确率-----------------------------) precision_score_average_None precision_score(y_test, y_pred, averageNone) precision_score_average_micro precision_score(y_test, y_pred, averagemicro) precision_score_average_macro precision_score(y_test, y_pred, averagemacro) precision_score_average_weighted precision_score(y_test, y_pred, averageweighted) print(precision_score_average_None , precision_score_average_None) print(precision_score_average_micro , precision_score_average_micro) print(precision_score_average_macro , precision_score_average_macro) print(precision_score_average_weighted , precision_score_average_weighted)print(\n\n----------------------------- recall召回率-----------------------------) recall_score_average_None recall_score(y_test, y_pred, averageNone) recall_score_average_micro recall_score(y_test, y_pred, averagemicro) recall_score_average_macro recall_score(y_test, y_pred, averagemacro) recall_score_average_weighted recall_score(y_test, y_pred, averageweighted) print(recall_score_average_None , recall_score_average_None) print(recall_score_average_micro , recall_score_average_micro) print(recall_score_average_macro , recall_score_average_macro) print(recall_score_average_weighted , recall_score_average_weighted)print(\n\n----------------------------- F1-value-----------------------------) f1_score_average_None f1_score(y_test, y_pred, averageNone) f1_score_average_micro f1_score(y_test, y_pred, averagemicro) f1_score_average_macro f1_score(y_test, y_pred, averagemacro) f1_score_average_weighted f1_score(y_test, y_pred, averageweighted) print(f1_score_average_None , f1_score_average_None) print(f1_score_average_micro , f1_score_average_micro) print(f1_score_average_macro , f1_score_average_macro) print(f1_score_average_weighted , f1_score_average_weighted) 结果 作者简介 读研期间发表6篇SCI数据挖掘相关论文现在某研究院从事数据算法相关科研工作结合自身科研实践经历不定期分享关于Python、机器学习、深度学习、人工智能系列基础知识与应用案例。致力于只做原创以最简单的方式理解和学习关注我一起交流成长。需要数据集和源码的小伙伴可以关注底部公众号添加作者微信。