Fig 1. Model evaluation and selection, Model and algorithm comparison
This is a short post created for quick reference on techniques which could be used for model evaluation & selection and model and algorithm comparision. This would be very helpful for those aspiring data scientists beginning to learn machine learning or those with advanced data science skills as well.
The image has been taken from this blog, Comparing the performance of machine learning models and algorithms using statistical tests and nested cross-validation authored by Dr. Sebastian Raschka
Fig 1. Model evaluation and selection, Model and algorithm comparison
The above diagram provides prescription for what needs to be done in each of the following areas with small and large dataset. Very helpful, indeed.
In recent years, artificial intelligence (AI) has evolved to include more sophisticated and capable agents,…
Adaptive learning helps in tailoring learning experiences to fit the unique needs of each student.…
With the increasing demand for more powerful machine learning (ML) systems that can handle diverse…
Anxiety is a common mental health condition that affects millions of people around the world.…
In machine learning, confounder features or variables can significantly affect the accuracy and validity of…
Last updated: 26 Sept, 2024 Credit card fraud detection is a major concern for credit…