Understanding Overfitting in Detail

Let's take a deeper look into the concept of Overfitting, which has appeared multiple times before.

Overfitting refers to a situation where an AI model fits the training data very well but is overly optimized for the training data, resulting in poor performance on new or validation data.

In simple terms, the model learns specific patterns in the training data, including noise (unnecessary information or random variations in the data), making it ineffective in general situations.

Let's assume a child is learning about dinosaurs.

At first, when they hear the word "Tyrannosaurus", they may only imagine "a giant animal with big teeth that walks on two legs."

Now, if the child is shown various dinosaur pictures and asked, "Can you pick out the Tyrannosaurus from these?", they may label all the big and scary-looking dinosaurs as Tyrannosaurus.

As time goes by, the child learns more details about Tyrannosaurus.

They gradually acquire specific details like tooth shape, number of toes, and body length.

However, if they learn only these overly specific characteristics, a problem arises.

For example, if they see another dinosaur with the same number of toes, they might mistakenly conclude, "It has the same number of toes, so it must be a Tyrannosaurus!"

This phenomenon, where excessive focus on specific features leads to mistaking other dinosaurs for a Tyrannosaurus, is called Overfitting.

Overfitting can be resolved using the following methods:

Transforming or adding data helps the model learn diverse data patterns.

For example, words in text can be replaced, or images can be rotated.

Overfitting can be mitigated by adjusting hyperparameters as follows:

Learning Rate

The learning rate determines how quickly or slowly the model adjusts its weights during training.

If the learning rate is too low, overfitting may occur, so an appropriate learning rate should be found.

Batch Size

Batch size refers to the amount of data processed at once during training.

A small batch size may cause unstable training but helps the model learn more diverse patterns.

On the other hand, a large batch size stabilizes training but increases the risk of overfitting.

Number of Epochs

The number of epochs represents how many times the entire dataset is processed during training.

Too many epochs can lead to overfitting.