Description: Non-linear models are models that do not assume a linear relationship between input and output variables. This means that the relationship between the variables can be more complex and cannot be adequately represented by a simple straight line. These models are capable of capturing patterns and behaviors in the data that are intrinsically non-linear, making them especially useful in a variety of applications where relationships are more complicated. Non-linear models can include techniques such as decision trees, neural networks, and support vector machines with non-linear kernels. One of the most notable features of these models is their flexibility, as they can adapt to different data shapes and underlying structures. However, this flexibility can also lead to a higher risk of overfitting, where the model fits too closely to the training data and loses the ability to generalize. Therefore, it is crucial to apply validation and regularization techniques when using non-linear models to ensure that a balance is maintained between model complexity and its ability to generalize to new data.
