overfitting

Vocabulary Word

Definition

'Overfitting' is a common issue in machine learning where the model is too specific to the training data. This is like studying for a test just by memorizing all the answers that might not help in different questions.

Examples in Different Contexts

In creative planning, while not commonly termed 'overfitting', a similar concept might be focusing too narrowly on specific inspirations. A creative planner might remark, 'While this concept fits past trends perfectly, we should avoid being overly influenced by them and explore fresh perspectives.'

Practice Scenarios

Scenario:

Our AI model performed exceptionally on the training data but failed to generalize the predictions for the unseen data. Some potential complexities added to the model might have led to this.

Response:

Indeed, reducing overfitting is essential to improve the generalizability of our AI model to various real-world situations.

Business

Scenario:

Our sales forecasting for the upcoming quarter seems to be unusually high. The sudden spike in sales figures from the previous holiday season might have impacted our prediction model.

Response:

Right, we need to address the problem of overfitting in our sales forecasting model to make it more adaptable to diverse market conditions.