Limitations of Learning Curves The learning curve model assumes that taking less and less time to do something is always good – and always possible. Typical applications are in manufacturing, construction, and document processing. Sometimes, however, a specific learning curve doesn't apply, especiall...
loss curves for deep networks can vary quite a bit in the course of normal training. Devising a rule that separates healthy variation from a marked downward trend can take significant effort. In practice, many practitioners just train models with differing (fixed) numbers of epochs, and choose ...
Information seeking must often be balanced against reward seeking. Examples of such reward-information trade-offs are rampant in human and animal environments. For example, long-tailed macaques will sometimes forego eating observable rewards to uncover and consume hidden ones first before returning to e...
Lift curves are very useful in marketing models where we want to predict whether a certain audience will react to a specific marketing campaign: using the lift curve we can make a cut in a certain population size that is the most likely to react positively. Also, they can be used for sim...
Empirically, learning curves are known to have a \(A{N}_{{\rm{train}}}^{-\beta }\) dependence70. In the training of neural networks typically \(1 < \beta < 2\)71, while we found values between 0.15 − 0.30 for the properties studied. Since different types of models can ...
Sometimes this will reveal unsuspected correlations or new trends, and thereby lead to a better understanding of the problem. Applying ML techniques to dig into large amounts of data can help discover patterns that were not immediately apparent. This is called data mining. Figure 1-4. Machine...
ROC curves are typically convex. The closer the curve is to the upper left and the larger the AUC area, the better the classification performance for minority categories (Huang et al.2019). Additionally, the ROC curve can also be applied to raise the recall by shifting the decision threshold...
You can also use this to think about where your team is collectively on the S-curve in terms of how you work together: “Are we making progress in our ability to cohere and move a project forward?” In general, however, I tend to look at this as an aggregation of individual curves,...
2.3 Learning Curves 2.4 Deciding What to Do Next Revisited 3 Programming Assignment: Regularized Linear Regression and Bias/Variance 4 Building a Spam classifier 4.1 Prioritizing What to Work On 4.2 Error Analysis 5 Handing Skewed Data 5.1 Error Metrics for Skewed Classes ...
If so, this indicates that we are in the "not compute-bound" regime and that we may be able to decrease the number of training steps. Although we cannot enumerate them all, there are many other additional behaviors that can become evident from examining the training curves (e.g. training...