Understanding Underfitting in Detail
In this lesson, we will delve deeper into the concept of Underfitting.
Underfitting occurs when an AI model fails to learn enough from the training data patterns, resulting in poor performance on both the training data and new data.
Understanding Underfitting Through an Analogy
Imagine a child starting to learn about dinosaurs.
Initially, when the child hears the word Tyrannosaurus, they only learn the basic image of a large animal with big teeth walking on two legs.
Now, suppose you show this child several dinosaur pictures and ask them to pick out the Tyrannosaurus. If the child has learned too little about dinosaurs, they might incorrectly identify the Tyrannosaurus. For example, they might mistakenly think a small dinosaur or one that walks on four legs is a Tyrannosaurus.
This child hasn't learned enough details about the Tyrannosaurus to properly distinguish it from other dinosaurs. This situation is what we call underfitting.
Solutions for Underfitting
1. Increase Model Complexity
Increase the complexity of the model so it can better learn the patterns in the data. For instance, by using a model with more features or applying a more systematic neural network.
2. Ensure Sufficient Training Data
Ensure the model learns from ample data by increasing the amount of training data. Using data that includes various situations and patterns helps the model learn more effectively.
3. Adjust Hyperparameters
Learning Rate
Adjust the learning rate properly to allow the model to learn adequately. A learning rate set too high can lead to unstable learning, while too low a rate can result in underfitting.
Batch Size
The batch size should be set appropriately so the model can sufficiently learn the data patterns. A batch size that is too small may lead to unstable learning, but one that is too large can cause underfitting.
Number of Epochs
Increase the number of epochs to allow the model to learn adequately. Too few epochs can lead to underfitting.
What does underfitting mean?
The model overlearns the training data and performs poorly on new data
The model fails to learn the patterns in the training data adequately, performing poorly on both the training and new data
The model perfectly learns the training data and performs excellently on new data
The model makes good predictions on new data based on the training data
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