Welcome! In this lesson, we're going to delve into an essential part of the data analysis and Machine Learning process — Model Evaluation — and specifically focus on understanding various evaluation metrics. In the language of Machine Learning, models are mathematical formulas, or algorithms, that process your input data to calculate the result for the task they're designed to perform. To ensure a model's predictions are accurate and reliable, we evaluate it against a set of standards or criteria, known as evaluation metrics.

Our primary goal in this lesson is to understand these metrics and learn how to apply them using Python and Sklearn on the Iris dataset. Given the numerous machine learning models available, knowing how to calculate and interpret these evaluation metrics will be crucial in selecting the most suitable model for any task. So, let's dive in!

In the fascinating world of Machine Learning, we often encounter a question -- "How well is our model performing?". The response to this question is provided through the process of model evaluation. Model evaluation allows us to quantify our model's performance, essentially telling us how 'good' or 'bad' it is.

A standard method for model evaluation is splitting our data into Training and Test Sets. By training our model on the Training Set and then testing it on the Test Set, we ensure that our evaluation is unbiased and indicative of how the model will perform on new, unseen data.

The concept of Cross-Validation further refines this process. In Cross-Validation, we divide our dataset into 'K' parts, or folds. We then train our model 'K' times, each time using a different fold as our Test Set. This yields 'K' performance scores, which we average to get a final score.

Let's take a look at how this plays out in code: