Summarizing other notable machine learning models

In the dynamic landscape of machine learning, a plethora of models cater to diverse data and problem domains. In this section, we will highlight other notable models, each offering unique capabilities and addressing specific challenges. From text processing to survival analysis, we’ll explore a spectrum of models that expand the horizons of machine learning applications.

So, let’s take a look:

• Generalized additive models (GAMs): GAMs extend linear regression by accommodating nonlinear relationships between variables. By employing smooth functions, GAMs offer a flexible framework to capture complex interactions and patterns in data, making them valuable tools for various domains, including environmental science, economics, and healthcare.
• Naïve Bayes: This is a probabilistic classifier grounded in Bayes’ theorem. Despite its simplicity, Naive Bayes excels in text classification, spam filtering...