What is the minimum number of records suggested for building reliable predictive models?

The suggestion of 10,000 or more records for building reliable predictive models is based on the idea that a larger data set provides more representative and diverse data, which is crucial for training robust models. With a minimum of 10,000 records, the statistical significance of the patterns identified in the data improves, helping to ensure that the models generalize better to unseen data. This size allows for the modeling of more complex relationships between variables and reduces the likelihood of overfitting, which can occur when there is not enough data to effectively capture the underlying trends.

Additionally, having a larger dataset enables better stratification of the data into training, validation, and testing subsets, which is essential for effectively assessing model performance and making necessary adjustments. Overall, the choice of 10,000 or more records reflects a well-established guideline in data science for achieving reliable predictive outcomes.