Simplify Model Testing: Track ML Model Training

Simplify Model Testing: Track ML Model Training

Table of Contents:

  1. Introduction
  2. Installing Dependencies
  3. Creating a Neptune Run
  4. Logging Model Summary
    1. Classification Summary
    2. Regression Summary
    3. Clustering Summary
  5. Custom Dashboards
  6. Logging Specific Method Data
  7. Conclusion
  8. Pros and Cons
  9. FAQ

Article:

Introduction

As a data scientist, working on machine learning models is a part of the job. Testing these models can be a tedious task, but the integration of scikit-learn and Neptune AI have made it simple to log and monitor your model training process. In this article, we will learn how to integrate scikit-learn and Neptune AI, and how it can help to simplify model testing.

Installing Dependencies

Before we start, we need to install the dependencies. Once the dependencies are installed, we can start associating the model and fit it.

Creating a Neptune Run

To work with this integration, the first thing we need to do is Create a Neptune run. This can be done by passing in the project name and API token to the Neptune init method. Optionally, You can pass in the name, description, tags, and other details. Once the run is created, it will be empty.

Logging Model Summary

The next step is to log a summary of the regressor. In this integration, there are three functions that can give a complete summary of classification, regression, and clustering algorithms for scikit-learn. All you need to do is pass in the model, your train and test data splits. In this case, We Are using a random forest regressor.

To log the summary, we will use the create regressor summary function, which will give us a summary of all the parameters that our model used. It will pickle our model, and give us diagnostic charts and test predictions and scores. We can see all the important attributes like feature importance, learning curve, prediction error, and residuals.

Custom Dashboards

A custom dashboard is a quick way to get a glimpse of your entire experiment to understand how your model is performing. You can add widgets to your dashboard to see errors, error values and understand whether it's performing correctly or not.

Logging Specific Method Data

You can also log a specific method data of your choice. In this case, we are fitting our random force classifier and creating a run. After that, we can log individual metadata such as estimator parameters and the pickled model. Finally, we can optionally log the confusion matrix to Neptune.

Conclusion

In conclusion, we have learned about the integration of scikit-learn and Neptune AI, how to log and monitor your model training process, and how it can help to simplify model testing. The process is straightforward, and custom dashboards give a quick and easy visual representation of the model summary.

Pros and Cons

Pros:

  • Neptune AI provides an easy and simple integration with scikit-learn.
  • Custom dashboards give a quick way to understand how your model is performing.
  • Neptune AI logs all metadata, including model parameters, diagnostic charts, and test predictions and scores.

Cons:

  • The integration may not support all machine learning algorithms.

FAQ

Q: What is Neptune AI? A: Neptune AI is a machine learning platform that helps teams to manage, monitor, and reproduce their machine learning experiments.

Q: What is scikit-learn? A: Scikit-learn is an open-source machine learning library that provides various tools for data preprocessing, classification, regression, and clustering algorithms.

Q: How can I install dependencies for the integration? A: You can install dependencies using the pip package manager.

Q: What is a custom dashboard? A: A custom dashboard is a quick way to get a glimpse of your entire experiment to understand how your model is performing visually.

Q: Does Neptune AI support all machine learning algorithms? A: No, the integration may not support all machine learning algorithms.

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