Welcome to our lesson on regularization, a pivotal concept in machine learning. Regularization is a technique used to prevent overfitting, a common issue that arises when our model learns too much detail from the training data and performs poorly on unseen data. In this lesson, we will focus on learning and applying L1 and L2 regularization techniques to Logistic Regression and Decision Tree models.

In this section, we'll explore how to tackle overfitting through regularization. Overfitting is like memorizing the answers to a test rather than understanding the subject. It happens when a model learns the training data too well, including its noise and outliers, which hampers its performance on new, unseen data. Regularization helps to prevent this by simplifying the model in a controlled way.

There are two main types of regularization techniques we will focus on: L1 (Lasso) and L2 (Ridge) regularization. Both methods add a penalty to the model, but they do so in different ways, leading to different outcomes.

Imagine you're painting a picture but decide to use only the essential colors. This is what L1 regularization does. It simplifies the model by forcing some feature weights to be exactly zero, effectively removing those features from the model. This can lead to a model that's easier to interpret and less prone to overfitting. In technical terms, L1 adds a penalty equal to the absolute value of the magnitude of coefficients.