Introduction
In the previous chapters, we walked through the steps that we need to take in a data science project before we can train a machine learning model. This included the planning phase, that is, identifying business problems, assessing data sources for suitability, and deciding on modeling approaches.
Having decided on a general modeling approach, we should be careful to avoid the common pitfalls of training ML models as we proceed with modeling. Firstly, remember that training data is very important. In fact, increasing the amount of training data can have a larger impact than model selection on scoring performance. One issue is that there may not be enough data available, which could make patterns difficult to find and cause models to perform poorly on testing data. Data quality also has a huge effect on model performance. Some possible issues include the following:
- Non-representative training data (sampling bias)
- Errors in the record sets (such as recorded...
