In modern times, we've formalized Ockham's razor into the fields of statistical learning theory and computational learning theory. These fields have developed generalization bounds--a statistical description of a model's ability to generalize to new data based on factors such as:

In modern times, Ockham's razor has been formalized into the fields of statistical learning theory and computational learning theory. These fields have developed what are known as generalization bounds. These are statistical descriptions of a model's ability to generalize to new data. The factors that influence these generalization bounds include:

1. The complexity of the model: A more complex model may fit the training data very well, but it may also overfit the data, meaning it won't generalize well to new data. On the other hand, a simpler model may not fit the training data as well, but it may generalize better to new data.

2. The size of the training data: The larger the training data, the better the model's ability to generalize to new data. This is because a larger training data set provides a more accurate representation of the underlying distribution of the data.

3. The noise in the data: If the data is noisy, the model may learn to fit the noise rather than the underlying distribution of the data. This would reduce its ability to generalize to new data.

4. The bias-variance tradeoff: A model with high bias makes strong assumptions about the data and may not fit the training data well, but it may generalize better to new data. A model with high variance makes fewer assumptions about the data and may fit the training data very well, but it may not generalize well to new data.

These factors are all taken into account when developing generalization bounds, which provide a statistical measure of a model's expected ability to generalize to new data.