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If you wish to run experiments on Kaggle you can import a notebook from Kaggle or from GitHub. Note that this allows to you to run the experiments on the Kaggle servers and it will upload the results to your Kaggle account automatically. ## Basic workflows We recommend that you follow the workflows in the [OpenML examples]( The examples are hosted on Kaggle. This includes: - the model validation workflow (configuring the task, building the model, estimating the error, cross-validating, and evaluating the models) - the submission workflow (building models, and submitting models to Kaggle) Other workflows may work for you but this is the most commonly used. ## Custom workflows You can make your own custom workflows. ## Model training and model evaluation We strongly recommend that you follow the [OpenML workflows]( We use an example of how to import a notebook from the main branch of Kaggle to do the model training and evaluation. The example uses [Kaggle notebook]( ## Data The data is usually in JSON or XML format. We recommend [this tutorial]( to get started. ## Directory structure To get started with Kaggle in the Notebooks mode, you need to clone the Github repository, and start a Python virtualenv. To do so, follow the instructions in the Readme file. Then run the following command to start a Python virtual environment with working directory `/opt/kaggle/notebooks/notebooks`. ``` ~/.virtualenvs/notebooks-jq9Ltf4/bin/pip install -r requirements.txt /opt/kaggle/notebooks/notebooks/examples/workflows/ To enable the Notebooks mode, you can add this line to the [notebook entry script](



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