Understanding Overfitting in Depth
Let's explore the concept of Overfitting, which we've encountered several times, in more detail.
Overfitting occurs when an AI model performs extremely well on the training data but overly optimizes itself to this data, resulting in poor performance on new or validation data.
Simply put, the model learns the specific patterns and even the noise (unnecessary information or random fluctuations in the data) in the training data, causing it to perform poorly in general situations.
Understanding Overfitting through an Analogy
Imagine a child just starting to learn about dinosaurs.
At first, when they hear the word "Tyrannosaurus," they might simply think of "a large, two-legged animal with big teeth."
Now, if you show the child several dinosaur pictures and ask, "Can you pick the Tyrannosaurus?" they might point to all the big, scary-looking ones.
As time passes, the child learns more details about Tyrannosaurus.
They start to learn specific details like tooth shape, number of toes, and body length.
However, focusing too much on these detailed features can become problematic.
For example, when they encounter a different dinosaur with the same number of toes, they might wrongly identify it as a "Tyrannosaurus because the toe count is the same!"
This phenomenon, where intense focus on particular characteristics leads to mistaking other dinosaurs for Tyrannosaurus, is called Overfitting.
Solutions for Overfitting
Overfitting can be addressed using the following methods.
1. Data Augmentation
Transforming or adding data helps the model learn from diverse data patterns.
For example, replacing words in text or rotating images are techniques that can be used.
2. Hyperparameter Tuning
Adjusting hyperparameters in the following ways can help resolve overfitting.
Learning Rate
The learning rate determines how quickly or slowly the model adjusts its weights during training.
If too low, overfitting may occur, so it's essential to find the appropriate learning rate.
Batch Size
Batch size refers to the amount of data processed at one time during training.
A small batch size can make learning unstable but encourages learning from diverse patterns.
Conversely, a large batch size stabilizes learning but increases the risk of overfitting.
Number of Epochs
The number of epochs indicates how many times the entire dataset is iterated over during training.
Too many epochs can lead to overfitting.
We have now explored the concept and solutions for overfitting.
In the next lesson, we will dive into Underfitting.
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