Running Machine Learning (ML) algorithms, gives us results about predictions which in turn gives us confidence about the topic. Understanding Machine Learning is a fitting topic for the Dunning-Kruger effect displayed in the graph below. Have you ever heard of the Dunning-Kruger Effect? If not, see this article https://www.verywellmind.com/an-overview-of-the-dunning-kruger-effect-4160740
Machine learning is a fancy topic and people want to show off their expertise on the topic. But very few can evaluate model performance, overfitting, and other model quality metrics. In this discussion, in 1 page (no introduction and no summary)
Discuss where you would place yourself in the graph.
What is your plan to improve yourself in terms of coding languages, ML platforms, and evaluating model performance ( I consider myself novice level)?
In addition, in short summarize how to prevent overfitting and the balance bias-variance tradeoff.
References:
article 1 https://machinelearningmastery.com/overfitting-and-underfitting-with-machine-learning-algorithms/
article 2 https://elitedatascience.com/overfitting-in-machine-learning