Insufficient Resources for Feature Generation in the Past Generation
- Lack of advanced technology: In the past generation, the technology was not as advanced as it is today. This limited the resources available for feature generation.
- Limited access to data: Data availability was a major issue in the past, which hindered the process of feature generation. Without sufficient data, it is challenging to create effective features.
- Manual feature engineering: Feature generation in the past often required manual effort, which was time-consuming and prone to errors. This limited the ability to generate a large number of features.
- Limited computational power: The computational power available in the past was not as robust as it is now. This constrained the ability to process large datasets and generate complex features.

Comparison with Present Generation
- Advanced technology: The present generation has access to more advanced technology such as machine learning algorithms and deep learning models, which make feature generation more efficient and effective.
- Big data availability: The present generation has access to a vast amount of data, thanks to the internet and advancements in data collection techniques. This abundance of data enables more comprehensive feature generation.
- Automated feature engineering: With the development of automated feature engineering tools and techniques, the present generation can generate a large number of features quickly and accurately.
- High computational power: The present generation benefits from high computational power, which allows for faster processing of data and complex feature generation algorithms.
In conclusion, the feature generation process in the past generation may have been limited by factors such as technology, data availability, manual efforts, and computational power. However, the present generation has access to more resources and advanced tools that support efficient and effective feature generation.