What are the main challenges in machine learning techniques?

What are the main challenges in machine learning techniques?

Some key challenges include data quality, model interpretability, overfitting, and computational costs.

Can you explain why data quality is so important?

Data quality is crucial because poor data can lead to inaccurate models, which ultimately affect decision-making.

What is model interpretability and why does it matter?

Model interpretability refers to how well we can understand and trust the predictions made by a model. It's important for accountability.

How can we address overfitting in machine learning?

Overfitting can be addressed by using techniques like cross-validation, regularization, and simplifying the model.

What are some computational costs associated with machine learning?

Computational costs can include processing power, memory usage, and time required for training models, especially with large datasets.