Understanding the complexity
Firstly, let’s acknowledge that ML is a complex field, and it’s not just about crunching numbers. It involves intricate algorithms, vast amounts of data, and the ability to interpret and apply the results in a meaningful way.
Imagine you’re a marketing executive at a consumer goods company. You have access to a wealth of customer data and want to use ML to predict which customers are most likely to buy your new product.
Sounds straightforward, right? But there are many places where complexity can come in. We will briefly explain some of the key considerations, then go into each one in more detail:
- Data quality and quantity: Is your data clean and representative of your target population? Do you have enough high-quality data?
- Model selection and tuning: Have you selected the appropriate model for your data? Have you correctly trained or fine-tuned your model?
- Overfitting and underfitting: Is your model too complex...
