教程:使用 Azure 机器学习中的自动化 ML 创建分类模型Tutorial: Create a classification model with automated ML in Azure Machine Learning

在本教程中,你将了解如何在 Azure 机器学习工作室中使用自动化机器学习在不编写任何代码的情况下创建一个简单的分类模型。In this tutorial, you learn how to create a simple classification model without writing a single line of code using automated machine learning in the Azure Machine Learning studio. 此分类模型预测某个金融机构的客户是否会认购定期存款产品。This classification model predicts if a client will subscribe to a fixed term deposit with a financial institution.

利用自动机器学习,可以自动完成耗时的任务。With automated machine learning, you can automate away time intensive tasks. 自动机器学习会快速循环访问算法和超参数的多个组合,以帮助你根据所选的成功指标找到最佳模型。Automated machine learning rapidly iterates over many combinations of algorithms and hyperparameters to help you find the best model based on a success metric of your choosing.

有关时序预测示例,请参阅教程:需求预测和自动化机器学习For a time-series forecasting example, see Tutorial: Demand forecasting & AutoML.

本教程介绍如何执行以下任务:In this tutorial, you learn how to do the following tasks:

  • 创建 Azure 机器学习工作区。Create an Azure Machine Learning workspace.
  • 运行自动机器学习试验。Run an automated machine learning experiment.
  • 查看试验详细信息。View experiment details.
  • 部署模型。Deploy the model.

先决条件Prerequisites

  • Azure 订阅。An Azure subscription. 如果没有 Azure 订阅,请创建一个试用帐户If you don't have an Azure subscription, create a trial account.

  • 下载 bankmarketing_train.csv 数据文件。Download the bankmarketing_train.csv data file. y 列指示客户是否认购了定期存款产品,该列稍后在本教程中将标识为预测目标列。The y column indicates if a customer subscribed to a fixed term deposit, which is later identified as the target column for predictions in this tutorial.

创建工作区Create a workspace

Azure 机器学习工作区是云中的基础资源,用于试验、训练和部署机器学习模型。An Azure Machine Learning workspace is a foundational resource in the cloud that you use to experiment, train, and deploy machine learning models. 它将 Azure 订阅和资源组关联到服务中一个易于使用的对象。It ties your Azure subscription and resource group to an easily consumed object in the service.

可以通过许多方法来创建工作区There are many ways to create a workspace. 本教程将通过 Azure 门户创建工作区,该门户是用于管理 Azure 资源的基于 Web 的控制台。In this tutorial, you create a workspace via the Azure portal, a web-based console for managing your Azure resources.

  1. 使用 Azure 订阅的凭据登录到 Azure 门户Sign in to Azure portal by using the credentials for your Azure subscription.

  2. 在 Azure 门户的左上角,选择“+ 创建资源” 。In the upper-left corner of Azure portal, select + Create a resource.

    创建新资源

  3. 使用搜索栏查找“机器学习” 。Use the search bar to find Machine Learning.

  4. 选择“机器学习” 。Select Machine Learning.

  5. 在“机器学习”窗格中,选择“创建”以开始 。In the Machine Learning pane, select Create to begin.

  6. 提供以下信息来配置新工作区:Provide the following information to configure your new workspace:

    字段Field 说明Description
    工作区名称Workspace name 输入用于标识工作区的唯一名称。Enter a unique name that identifies your workspace. 本示例使用 docs-ws 。In this example, we use docs-ws. 名称在整个资源组中必须唯一。Names must be unique across the resource group. 使用易于记忆且区别于其他人所创建工作区的名称。Use a name that's easy to recall and to differentiate from workspaces created by others.
    订阅Subscription 选择要使用的 Azure 订阅。Select the Azure subscription that you want to use.
    资源组Resource group 使用订阅中的现有资源组,或者输入一个名称以创建新的资源组。Use an existing resource group in your subscription or enter a name to create a new resource group. 资源组保存 Azure 解决方案的相关资源。A resource group holds related resources for an Azure solution. 本示例使用 docs-aml 。In this example, we use docs-aml.
    位置Location 选择离你的用户和数据资源最近的位置来创建工作区。Select the location closest to your users and the data resources to create your workspace.
    工作区版本Workspace edition 选择“基本” 作为本教程的工作区类型。Select Basic as the workspace type for this tutorial. 工作区类型(基本和企业)确定要访问的功能和定价。The workspace type (Basic & Enterprise) determines the features to which you’ll have access and pricing. 本教程中的所有内容均可使用基本或企业工作区来执行。Everything in this tutorial can be performed with either a Basic or Enterprise workspace.
  7. 完成工作区配置后,选择“查看 + 创建” 。After you are finished configuring the workspace, select Review + Create.

    警告

    在云中创建工作区可能需要几分钟时间。It can take several minutes to create your workspace in the cloud.

    完成创建后,会显示部署成功消息。When the process is finished, a deployment success message appears.

  8. 若要查看新工作区,请选择“转到资源” 。To view the new workspace, select Go to resource.

重要

记下你的工作区和订阅 。Take note of your workspace and subscription. 你将需要这些项才能确保在正确的位置创建试验。You'll need these to ensure you create your experiment in the right place.

在 Azure 机器学习工作室中开始操作Get started in Azure Machine Learning studio

通过 https://studio.ml.azure.cn 处的 Azure 机器学习工作室完成以下试验设置和运行步骤,这是一个综合性的 Web 界面,其中包括了为所有技能级别的数据科学实践者执行数据科学方案所需的机器学习工具。You complete the following experiment set-up and run steps via the Azure Machine Learning studio at https://studio.ml.azure.cn, a consolidated web interface that includes machine learning tools to perform data science scenarios for data science practitioners of all skill levels. Internet Explorer 浏览器不支持此工作室。The studio is not supported on Internet Explorer browsers.

  1. 登录到 Azure 机器学习Sign in to Azure Machine Learning.

  2. 选择创建的订阅和工作区。Select your subscription and the workspace you created.

  3. 选择“开始”。 Select Get started.

  4. 在左窗格的“创作”部分,选择“自动化 ML” 。In the left pane, select Automated ML under the Author section.

    由于这是你的第一个自动化 ML 试验,因此会看到空列表和文档链接。Since this is your first automated ML experiment, you'll see an empty list and links to documentation.

    “入门”页

  5. 选择“+新建自动化 ML 运行”。Select +New automated ML run.

创建并加载数据集Create and load dataset

在配置试验之前,请以 Azure 机器学习数据集的形式将数据文件上传到工作区。Before you configure your experiment, upload your data file to your workspace in the form of an Azure Machine Learning dataset. 这可以确保数据格式适合在试验中使用。Doing so, allows you to ensure that your data is formatted appropriately for your experiment.

  1. 通过从“+ 创建数据集”下拉菜单选择“从本地文件”,创建新的数据集。Create a new dataset by selecting From local files from the +Create dataset drop-down.

    1. 在“基本信息”窗体中,为数据集指定名称,并提供可选的说明。On the Basic info form, give your dataset a name and provide an optional description. 自动化 ML 当前仅支持 TabularDataset,因此,数据集类型应当默认设置为“表格”。The automated ML interface currently only supports TabularDatasets, so the dataset type should default to Tabular.

    2. 在左下角选择“下一步”Select Next on the bottom left

    3. 在“数据存储和文件选择”窗体上,选择在创建工作区期间自动设置的默认数据存储“workspaceblobstore(Azure Blob 存储)”。On the Datastore and file selection form, select the default datastore that was automatically set up during your workspace creation, workspaceblobstore (Azure Blob Storage). 你可以在此数据存储中上传数据文件,使其可用于你的工作区。This is where you'll upload your data file to make it available to your workspace.

    4. 选择“浏览”。Select Browse.

    5. 选择本地计算机上的 bankmarketing_train.csv 文件。Choose the bankmarketing_train.csv file on your local computer. 这是作为必备组件下载的文件。This is the file you downloaded as a prerequisite.

    6. 为数据集指定唯一名称,并提供可选说明。Give your dataset a unique name and provide an optional description.

    7. 在底部左侧选择“下一步”,将其上传到在创建工作区期间自动设置的默认容器。Select Next on the bottom left, to upload it to the default container that was automatically set up during your workspace creation.

      上传完成后,系统会根据文件类型预先填充“设置和预览”窗体。When the upload is complete, the Settings and preview form is pre-populated based on the file type.

    8. 验证“设置和预览”窗体是否已填充如下,然后选择“下一步”。Verify that the Settings and preview form is populated as follows and select Next.

      字段Field 说明Description 教程的值Value for tutorial
      文件格式File format 定义文件中存储的数据的布局和类型。Defines the layout and type of data stored in a file. 带分隔符Delimited
      分隔符Delimiter 一个或多个字符,用于指定纯文本或其他数据流中不同的独立区域之间的边界。 One or more characters for specifying the boundary between  separate, independent regions in plain text or other data streams. 逗号Comma
      编码Encoding 指定字符架构表中用于读取数据集的位。Identifies what bit to character schema table to use to read your dataset. UTF-8UTF-8
      列标题Column headers 指示如何处理数据集的标头(如果有)。Indicates how the headers of the dataset, if any, will be treated. 所有文件都具有相同的标题All files have same headers
      跳过行Skip rows 指示要跳过数据集中的多少行(如果有)。Indicates how many, if any, rows are skipped in the dataset. None
    9. 通过“架构”窗体,可以进一步为此试验配置数据。The Schema form allows for further configuration of your data for this experiment. 对于本示例,为 day_of_week 选择切换开关,以使其不包含在内。For this example, select the toggle switch for the day_of_week, so as to not include it. 选择“下一页”。Select Next. 架构窗体Schema form

    10. 在“确认详细信息”窗体上,确认信息与先前在“基本信息”、“数据存储和文件选择”和“设置和预览”窗体上填充的内容匹配。 On the Confirm details form, verify the information matches what was previously populated on the Basic info, Datastore and file selection and Settings and preview forms.

    11. 选择“创建”以完成数据集的创建。Select Create to complete the creation of your dataset.

    12. 当数据集出现在列表中时,则选择它。Select your dataset once it appears in the list.

    13. 查看“数据预览”,以确保未包括“day_of_week”,然后选择“关闭”。Review the Data preview to ensure you didn't include day_of_week then, select Close.

    14. 选择“下一步”。Select Next.

配置运行Configure run

加载并配置数据后,可以设置试验。After you load and configure your data, you can set up your experiment. 此设置包括试验设计任务,如选择计算环境大小以及指定要预测的列。This setup includes experiment design tasks such as, selecting the size of your compute environment and specifying what column you want to predict.

  1. 选择“新建”单选按钮。Select the Create new radio button.

  2. 按如下所示填充“配置运行”窗体:Populate the Configure Run form as follows:

    1. 输入以下试验名称:my-1st-automl-experimentEnter this experiment name: my-1st-automl-experiment

    2. 选择“y”作为用于执行预测的目标列。Select y as the target column, what you want to predict. 此列指示客户是否认购了定期存款产品。This column indicates whether the client subscribed to a term deposit or not.

    3. 选择“+创建新计算”并配置计算目标。Select +Create a new compute and configure your compute target. 计算目标是本地的或基于云的资源环境,用于运行训练脚本或托管服务部署。A compute target is a local or cloud-based resource environment used to run your training script or host your service deployment. 对于此试验,我们使用基于云的计算。For this experiment, we use a cloud-based compute.

      1. 填充“虚拟机”窗体以设置计算。Populate the Virtual Machine form to set up your compute.

        字段Field 说明Description 教程的值Value for tutorial
        虚拟机优先级  Virtual machine priority 选择试验应具有的优先级Select what priority your experiment should have 专用Dedicated
        虚拟机类型  Virtual machine type 选择计算的虚拟机大小。Select the virtual machine type for your compute. CPU(中央处理单元)CPU (Central Processing Unit)
        虚拟机大小  Virtual machine size 指定计算资源的虚拟机大小。Select the virtual machine size for your compute. 根据数据和试验类型提供了建议的大小列表。A list of recommended sizes is provided based on your data and experiment type. Standard_DS12_V2Standard_DS12_V2
      2. 选择“下一步”以填充“配置设置窗体”。Select Next to populate the Configure settings form.

        字段Field 说明Description 教程的值Value for tutorial
        计算名称Compute name 用于标识计算上下文的唯一名称。A unique name that identifies your compute context. automl-computeautoml-compute
        最小/最大节点数Min / Max nodes 若要分析数据,必须指定一个或多个节点。To profile data, you must specify 1 or more nodes. 最小节点数:1Min nodes: 1
        最大节点数:6Max nodes: 6
        缩减前的空闲秒数Idle seconds before scale down 群集自动缩减到最小节点数之前的空闲时间。Idle time before the cluster is automatically scaled down to the minimum node count. 120(默认值)120 (default)
        高级设置Advanced settings 用于为试验配置虚拟网络并对其进行授权的设置。Settings to configure and authorize a virtual network for your experiment. None
      3. 选择“创建”,创建计算目标。Select Create to create your compute target.

        完成此操作需要数分钟的时间。This takes a couple minutes to complete.

        “设置”页面

      4. 创建后,从下拉列表中选择新的计算目标。After creation, select your new compute target from the drop-down list.

    4. 选择“下一步”。Select Next.

  3. 在“任务类型和设置”窗体上,通过指定机器学习任务类型和配置设置来完成自动化 ML 试验的设置。On the Task type and settings form, complete the setup for your automated ML experiment by specifying the machine learning task type and configuration settings.

    1. 选择“分类”作为机器学习任务类型。Select Classification as the machine learning task type.

    2. 选择“查看其他配置设置”并按如下所示填充字段。Select View additional configuration settings and populate the fields as follows. 使用这些设置可以更好地控制训练作业。These settings are to better control the training job. 否则,将会根据试验选择和数据应用默认设置。Otherwise, defaults are applied based on experiment selection and data.

      其他配置 Additional configurations 说明Description 教程的值  Value for tutorial
      主要指标Primary metric 对机器学习算法进行度量时依据的评估指标。Evaluation metric that the machine learning algorithm will be measured by. AUC_weightedAUC_weighted
      解释最佳模型Explain best model 自动显示有关自动化 ML 创建的最佳模型的可解释性。Automatically shows explainability on the best model created by automated ML. 启用Enable
      阻止的算法Blocked algorithms 要从训练作业中排除的算法Algorithms you want to exclude from the training job None
      退出条件Exit criterion 如果符合某个条件,则会停止训练作业。If a criteria is met, the training job is stopped. 训练作业时间(小时):  1Training job time (hours): 1
      指标分数阈值:  无Metric score threshold: None
      验证Validation 选择交叉验证类型和测试数。Choose a cross-validation type and number of tests. 验证类型:Validation type:
      k-折交叉验证   k-fold cross-validation

      验证次数:2Number of validations: 2
      并发Concurrency 每次迭代执行的并行迭代的最大数目The maximum number of parallel iterations executed per iteration 最大并发迭代次数:  5Max concurrent iterations: 5

      选择“保存”。Select Save.

  4. 选择“完成”以运行试验。Select Finish to run the experiment. 当试验准备开始时,将打开“运行详细信息”屏幕并且会在顶部显示“运行状态”。The Run Detail screen opens with the Run status at the top as the experiment preparation begins. 此状态随着试验的进行而更新。This status updates as the experiment progresses. 通知也会显示在工作室的右上角,以告知你试验的状态。Notifications also appear in the top right corner of the studio, to inform you of the status of your experiment.

重要

准备试验运行时,准备需要 10-15 分钟Preparation takes 10-15 minutes to prepare the experiment run. 运行以后,每个迭代还需要 2-3 分钟Once running, it takes 2-3 minutes more for each iteration.

在生产环境中,你可能会走开一段时间。In production, you'd likely walk away for a bit. 但在本教程中,建议你开始浏览“模型”选项卡上的已测试算法,因为当其他模型仍在运行的时候,这些模型已经完成。But for this tutorial, we suggest you start exploring the tested algorithms on the Models tab as they complete while the others are still running.

浏览模型Explore models

导航到“模型”选项卡,以查看测试的算法(模型)。Navigate to the Models tab to see the algorithms (models) tested. 默认情况下,这些模型在完成后按指标分数排序。By default, the models are ordered by metric score as they complete. 对于本教程,列表中首先显示评分最高的模型(评分根据所选 AUC_weighted 指标给出)。For this tutorial, the model that scores the highest based on the chosen AUC_weighted metric is at the top of the list.

在等待所有试验模型完成的时候,可以选择已完成模型的“算法名称”,以便浏览其性能详细信息。While you wait for all of the experiment models to finish, select the Algorithm name of a completed model to explore its performance details.

以下示例将浏览“详细信息”和“指标”选项卡,以查看选定模型的属性、指标和性能图表。 The following navigates through the Details and the Metrics tabs to view the selected model's properties, metrics, and performance charts.

运行迭代详细信息

模型说明Model explanations

在等待模型完成时,你还可以查看模型说明,了解哪些数据特征(原始的或经过工程处理的)影响特定模型的预测。While you wait for the models to complete, you can also take a look at model explanations and see which data features (raw or engineered) influenced a particular model's predictions.

可以按需生成这些模型说明,“说明(预览版)”选项卡的模型说明仪表板中汇总了这些模型说明。These model explanations can be generated on demand, and are summarized in the model explanations dashboard that's part of the Explanations (preview) tab.

若要生成模型说明,请执行以下操作:To generate model explanations,

  1. 选择顶部的“运行 1”导航回“模型”屏幕。Select Run 1 at the top to navigate back to the Models screen.

  2. 选择“模型”选项卡。Select the Models tab.

  3. 对于本教程,请选择第一个“MaxAbsScaler, LightGBM”模型。For this tutorial, select the first MaxAbsScaler, LightGBM model.

  4. 选择顶部的“说明模型”按钮。Select the Explain model button at the top. 此时右侧会显示“说明模型”窗格。On the right, the Explain model pane appears.

  5. 选择你之前创建的“automl-compute”。Select the automl-compute that you created previously. 此计算群集会启动一个子运行来生成模型说明。This compute cluster initiates a child run to generate the model explanations.

  6. 选择底部的“创建”。Select Create at the bottom. 屏幕顶部会出现一条绿色的成功消息。A green success message appears towards the top of your screen.

    备注

    模型说明运行需要大约 2-5 分钟才能完成。The explainability run takes about 2-5 minutes to complete.

  7. 选择“说明(预览版)”按钮。Select the Explanations (preview) button. 在模型说明运行完成后,此选项卡就会进行填充。This tab populates once the explainability run completes.

  8. 在左侧展开该窗格,然后在“特征”下选择显示了“原始”的行。On the left hand side, expand the pane and select the row that says raw under Features.

  9. 选择右侧的“聚合特征重要性”选项卡。Select the Aggregate feature importance tab on the right. 此图表显示了影响所选模型的预测的数据特征。This chart shows which data features influenced the predictions of the selected model.

    在此示例中,“持续时间”看起来对此模型的预测影响最大。In this example, the duration appears to have the most influence on the predictions of this model.

    模型说明仪表板

部署最佳模型Deploy the best model

使用自动化机器学习界面,你可以通过几个步骤将最佳模型部署为 Web 服务。The automated machine learning interface allows you to deploy the best model as a web service in a few steps. 部署是模型的集成,因此它可以对新数据进行预测并识别潜在的机会领域。Deployment is the integration of the model so it can predict on new data and identify potential areas of opportunity.

对于本试验,部署到 Web 服务意味着金融机构现已获得一个迭代和可缩放的 Web 解决方案,用于识别潜在的定期存款客户。For this experiment, deployment to a web service means that the financial institution now has an iterative and scalable web solution for identifying potential fixed term deposit customers.

检查试验运行是否完成。Check to see if your experiment run is complete. 为此请选择屏幕顶部的“运行 1”导航回父运行页。To do so, navigate back to the parent run page by selecting Run 1 at the top of your screen. “已完成”状态将显示在屏幕的左上角。A Completed status is shown on the top left of the screen.

试验运行完成后,“详细信息”页中会填充“最佳模型摘要”部分。 Once the experiment run is complete, the Details page is populated with a Best model summary section. 在此试验上下文中,根据 AUC_weighted 指标,VotingEnsemble 被视为最佳模型。In this experiment context, VotingEnsemble is considered the best model, based on the AUC_weighted metric.

我们将部署此模型,但请注意,部署需要大约 20 分钟才能完成。We deploy this model, but be advised, deployment takes about 20 minutes to complete. 部署过程需要几个步骤,包括注册模型、生成资源和为 Web 服务配置资源。The deployment process entails several steps including registering the model, generating resources, and configuring them for the web service.

  1. 选择“VotingEnsemble”打开特定于模型的页面。Select VotingEnsemble to open the model-specific page.

  2. 选择左上方的“部署”按钮。Select the Deploy button in the top-left.

  3. 按如下所示填充“部署模型”窗格:Populate the Deploy a model pane as follows:

    字段Field Value
    部署名称Deployment name my-automl-deploymy-automl-deploy
    部署说明Deployment description 我的第一个自动化机器学习试验部署My first automated machine learning experiment deployment
    计算类型Compute type 选择“Azure 计算实例(ACI)”Select Azure Compute Instance (ACI)
    启用身份验证Enable authentication 禁用。Disable.
    使用自定义部署Use custom deployments 禁用。Disable. 允许自动生成默认驱动程序文件(评分脚本)和环境文件。Allows for the default driver file (scoring script) and environment file to be autogenerated.

    本示例使用“高级”菜单中提供的默认值。For this example, we use the defaults provided in the Advanced menu.

  4. 选择“部署”。Select Deploy.

    “运行”屏幕的顶部会以绿色字体显示一条成功消息,“模型摘要”窗格中的“部署状态”下会显示一条状态消息。 A green success message appears at the top of the Run screen, and in the Model summary pane, a status message appears under Deploy status. 定期选择“刷新”以检查部署状态。Select Refresh periodically to check the deployment status.

现在,你已获得一个正常运行的、可以生成预测结果的 Web 服务。Now you have an operational web service to generate predictions.

转到后续步骤详细了解如何使用新的 Web 服务,以及如何使用 Power BI 的内置 Azure 机器学习支持来测试预测。Proceed to the Next Steps to learn more about how to consume your new web service, and test your predictions using Power BI's built in Azure Machine Learning support.

清理资源Clean up resources

部署文件比数据文件和试验文件更大,因此它们的存储成本也更大。Deployment files are larger than data and experiment files, so they cost more to store. 仅当你想要最大程度地降低帐户成本,或者想要保留工作区和试验文件时,才删除部署文件。Delete only the deployment files to minimize costs to your account, or if you want to keep your workspace and experiment files. 否则,如果你不打算使用任何文件,请删除整个资源组。Otherwise, delete the entire resource group, if you don't plan to use any of the files.

删除部署实例Delete the deployment instance

若要保留资源组和工作区以用于其他教程和探索,请从 Azure 机器学习 (https://studio.ml.azure.cn/) 中仅删除部署实例。Delete just the deployment instance from Azure Machine Learning at https://studio.ml.azure.cn/, if you want to keep the resource group and workspace for other tutorials and exploration.

  1. 转到 Azure 机器学习Go to Azure Machine Learning. 导航到你的工作区,然后在“资产”窗格的左下角选择“终结点”。Navigate to your workspace and on the left under the Assets pane, select Endpoints.

  2. 选择要删除的部署,然后选择“删除”。Select the deployment you want to delete and select Delete.

  3. 选择“继续”。Select Proceed.

删除资源组Delete the resource group

重要

已创建的资源可以用作其他 Azure 机器学习教程和操作方法文章的先决条件。The resources you created can be used as prerequisites to other Azure Machine Learning tutorials and how-to articles.

如果不打算使用已创建的资源,请删除它们,以免产生任何费用:If you don't plan to use the resources you created, delete them, so you don't incur any charges:

  1. 在 Azure 门户中,选择最左侧的“资源组” 。In the Azure portal, select Resource groups on the far left.

    在 Azure 门户中删除Delete in the Azure portal

  2. 从列表中选择已创建的资源组。From the list, select the resource group you created.

  3. 选择“删除资源组” 。Select Delete resource group.

  4. 输入资源组名称。Enter the resource group name. 然后选择“删除” 。Then select Delete.

后续步骤Next steps

在本自动化机器学习教程中,你已使用 Azure 机器学习的自动化 ML 界面创建并部署了一个分类模型。In this automated machine learning tutorial, you used Azure Machine Learning's automated ML interface to create and deploy a classification model. 有关详细信息和后续步骤,请参阅以下文章:See these articles for more information and next steps:

备注

此银行营销数据集是根据 Creative Commons (CCO:Public Domain) 许可条款提供的。This Bank Marketing dataset is made available under the Creative Commons (CCO: Public Domain) License. 数据库各项内容中的任何权利是根据数据库内容许可条款Kaggle 上授予的。Any rights in individual contents of the database are licensed under the Database Contents License and available on Kaggle. 此数据集最初在 UCI 机器学习数据库中提供。This dataset was originally available within the UCI Machine Learning Database.

[Moro et al., 2014] S. Moro, P. Cortez and P. Rita.[Moro et al., 2014] S. Moro, P. Cortez and P. Rita. A Data-Driven Approach to Predict the Success of Bank Telemarketing.A Data-Driven Approach to Predict the Success of Bank Telemarketing. Decision Support Systems, Elsevier, 62:22-31, June 2014.Decision Support Systems, Elsevier, 62:22-31, June 2014.