Understanding Overfitting in Detail
Let's dive deeper into the concept of Overfitting, which has appeared several times before.
Overfitting occurs when an AI model is highly attuned to the training data, making it overly optimized for that data set, but performs poorly on new or validation data.
Simply put, the model learns specific patterns in the training data, including noise (unnecessary information or random fluctuations contained in data), which makes it ineffective in general situations.
Understanding Overfitting through Analogies
Imagine a child starting to learn about dinosaurs.
At first, upon hearing the word Tyrannosaurus, they might only associate it with the image of a large animal with big teeth walking on two legs.
If you show this child a few dinosaur pictures and ask them to pick out the Tyrannosaurus, they may point to all the big and scary-looking ones.
However, as the child learns more about the Tyrannosaurus, they might start to overlearn specific details, like the shape of its teeth, the exact number of toes, or specific body lengths. At this point, they might misidentify other kinds of dinosaurs or similar-looking animals as a Tyrannosaurus.
Even if it's not actually a Tyrannosaurus, the child might think everything that fits their overly specific criteria is a Tyrannosaurus. This state is what we refer to as Overfitting.
Solutions to Overfitting
1. Data Augmentation
Transform or add data to help the model learn various data patterns. For example, swapping words in a text or rotating an image.
2. Hyperparameter Tuning
Learning Rate
The learning rate determines how quickly or slowly the model adjusts its weights during training. If it's too low, overfitting can occur, so finding an appropriate rate is key.
Batch Size
Batch size refers to the amount of data the model trains on at one time. A smaller batch size can lead to unstable training but assists in learning a wider range of patterns. Conversely, a larger batch size can stabilize training but may increase the risk of overfitting.
Number of Epochs
The number of epochs indicates how many times the model goes through the entire dataset. Too many epochs may lead to overfitting.
3. Use of Regularization Techniques
Apply constraints to prevent the model from relying too heavily on specific data, thereby preventing overfitting.
Dropout
Exclude some neurons randomly during the learning process to prevent overfitting.
Weight Decay
Prevent the model from having too large weights to encourage learning more generalized patterns.
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