Welcome to our in-depth exploration of recognizing the strengths and limitations of various machine learning models. We're going to focus on three key models — Linear Regression, Logistic Regression, and Decision Trees. By the end of this lesson, you should have a clear understanding of these foundational machine learning models, their strengths, and limitations, and how to interpret these characteristics when leveraging these models on datasets like the Iris dataset. You'll also gain insights into why being aware of these strengths and limitations is crucial when applying these models.

As a refresher, let's walk through the foundations of Linear Regression, Logistic Regression, and Decision Trees. Here's a quick overview of the basic principles of each model:

Linear Regression models are typically used when we want to predict a continuous or real-value output, such as predicting the price of a house based on its features or the amount of rainfall based on changes in temperature. Given an input feature, Linear Regression will model the relationship between this feature and its corresponding output using a best fit straight line.

Here's a basic code setup in Python for representing a Linear Regression model using the sklearn library: