What are the main challenges in achieving high accuracy in AI models?

What are the main challenges in achieving high accuracy in AI models?

Some key challenges include data quality, model complexity, overfitting, and the need for extensive training data.

How does data quality affect accuracy?

Data quality directly impacts model performance; poor quality data can lead to incorrect predictions and lower accuracy.

What is overfitting and how can it be avoided?

Overfitting occurs when a model learns noise in the training data. It can be avoided by using techniques like cross-validation and regularization.

Can you explain model complexity?

Model complexity refers to the number of parameters in a model. A more complex model can capture more patterns but may also overfit.

What role does training data size play in accuracy?

Larger training datasets generally improve accuracy by providing more examples for the model to learn from, reducing variance.