Underfitting and Overfitting in Practice

In short

• •Overfitting occurs when a model has excess capacity to memorize training data, causing validation error to increase while training error decreases.
• •Underfitting happens when a model is too simple to learn the data, resulting in high training and validation errors.
• •The session uses the IMDb movie review dataset in TensorFlow Keras to demonstrate baseline, underfitting, and overfitting scenarios.

Key concepts

• Overfitting0:10
• Underfitting0:58
• IMDb Dataset Lab Setup1:13
• TensorFlow Keras Environment2:09

Check yourself

1. 1. When does overfitting occur in a machine learning model?

   Answer: When the model has excess capacity to memorize the training data.

   Overfitting happens when the model has excess capacity to memorize the entire training data. 0:10

2. 2. What learning curve behavior indicates that a model is suffering from overfitting?

   Answer: Training error goes down while validation error starts climbing up.

   Overfitting is indicated when training error goes down but validation error starts climbing up. 0:30

3. 3. Which characteristic is typical of a model experiencing underfitting?

   Answer: Both training and validation errors are high.

   In the case of underfitting, both training and validation errors are high. 0:58

4. 4. Which dataset is used in this lab session to demonstrate underfitting and overfitting?

   Answer: IMDb movie review dataset

   The lab uses the IMDb movie review dataset to demonstrate underfitting and overfitting. 1:13

5. 5. Which tools are imported along with Keras for data manipulation and visualization?

   Answer: NumPy and Matplotlib

   NumPy and Matplotlib are imported alongside Keras for data manipulation and visualization. 2:09