Regulatory Alerts2019-12-22
A circular dated December 19, 2019 regarding the real estate financing initiative for middle-income people
To summarize, the performance of a machine learning model is highly dependent on the amount of labeled training data it has access to. Here's how different percentages of such data would affect the model's performance: - **90% - 100%**: These are ideal scenarios where we have sufficient labeled data for our model. As the percentage of available labeled data increases, the model can improve its ability to generalize and perform better on unseen data. - **70% - 89%**: This range is still good for training a machine learning model, but it might not be enough in some cases. The model might struggle with overfitting or underfitting issues depending on the complexity of your algorithm and the nature of your dataset. - **60% - 69%**: These percentages are closer to the edge. In this range, you need more data or use sophisticated techniques like ensemble methods, etc., to handle these issues effectively. - **0% - 59%** : This is a critical situation where we have very little labeled training data available for our machine learning model. The performance of your machine learning model would be significantly compromised in this case due to overfitting or underfitting issues, especially if the task you want to solve requires higher levels of abstraction (such as natural language processing tasks). Remember that each specific scenario will have different outcomes and solutions may vary. It's also important to consider other factors like data distribution, class imbalance, dimensionality reduction, etc., which can significantly affect your machine learning model's overall performance and optimization process.