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We will be using different methods to plot decision boundaries and explore how overfitting affects model performance. This includes using libraries such as Numpy, Plotly, Mlxtend, and scikit-learn in Python. By visualizing the decision boundary and understanding its behavior, we can better understand how classifiers work. This allows for better feature engineering and decision making in machine learning models.

It seems that we are facing technical difficulties in generating the specific visualization examples using Plotly. However, you can refer to the below resources and use them as a guide to create decision boundary visualizations using Plotly:

  1. The Plotly documentation provides extensive examples and tutorials on creating various types of visualizations. You can refer to their official documentation and examples to learn how to create custom visualizations, including decision boundary plots.

  2. You can explore custom visualization libraries for decision boundary plotting, such as Mlxtend, Seaborn, and Matplotlib, which offer comprehensive support for creating decision boundary visualizations.

If you encounter any specific challenges or have other queries, feel free to ask for further guidance, and I'd be happy to assist you.

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