Machine Learning Explainability Kaggle

Nevertheless it is too tempting to access the capabilities of machine learning algorithms that can offer high accuracy. Some see machine learning models as black boxes that are difficult to interpret and explain.

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So I used Kaggle courses to learn some basics around data science as a beginner using this approach to get going with my projects is the best way.

Machine learning explainability kaggle. Id emphasize learning from others. Team up with people in competitions or share your notebooks broadly to get feedback and advice from others. In case you want a real hang of this topic you can try the Machine Learning Explainability crash course from Kaggle.

In case you want a real hang of this topic you can try the Machine Learning Explainability crash course from Kaggle. Welcome to the Machine Learning Explainability course discussion. We can talk about the trade-off between accuracy and explainability.

It has the right amount of theory and code to put the concepts into perspective and helps to apply model explainability concepts to practical real-world problems. Fortunately Kaggle is a great place to learn. Find datasets about topics you find interesting and create your own projects to share.

Kaggle is a platform where you can learn a lot about machine learning with Python and R do data science projects and this is the most fun part join machine learning competitions. I can highly recommend this course as I have learned a lot of useful methods to analyse a trained ML model. Kaggles probably the best place in the world to learn by doing.

Theory only makes sense as long as we can put it into practice. For a brief overview of the topics covered this blog post will summarize my learnings. Model Explainability techniques.

For this challenge youre encouraged to use concepts such as feature importance perturbation importance and partial dependence plots to explain the predictions of an ML model. To begin please read our Kaggle Community Guidelines. Certificate recognizing that desertnaut has successfully completed the Kaggle course Machine Learning Explainability.

Competitions are changed and updated over time. It has the right amount of theory and code to put the concepts into perspective and helps to apply model explainability concepts to practical real-world problems. Then chat with other learners below.

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