Training and Testing

Training and Testing

When building a machine learning model, we don't use all our data at once. Instead, we divide it into different sets:

• Training Set: This is the data we use to train our model. The algorithm learns patterns from this data.
• Validation Set: After training, we use this separate set of data to tune our model parameters and evaluate its performance during development.
• Test Set: Finally, we use an independent set of data that the model has never seen before to evaluate its final performance. This gives us an estimate of how well the model will perform in the real world.

Important Note: We never use the test set to tune our model! Once we look at the test set results, we're done. Using the test set to improve the model would be like peeking at the answers before taking a test.

The Overfitting Problem

One of the biggest challenges in machine learning is balancing model complexity with generalization. This is where the concepts of overfitting and underfitting come in:

Underfitting:

• A model is underfitting when it's too simple to capture the underlying pattern in the data. It performs poorly on both the training and test data.
• Example: Using a straight line to model data that clearly follows a curve.

Overfitting:

• A model is overfitting when it's so complex that it learns not just the underlying pattern but also the random noise in the training data. It performs extremely well on the training data but poorly on new, unseen data.
• Example: Creating a complex, wiggly line that touches every single data point perfectly, but fails to capture the general trend.

The "Just Right" Model:

• The ideal model captures the true pattern in the data without being distracted by noise. Finding this balance is key to creating models that perform well in the real world.