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- Understand Precision in Predictive Analytics to Avoid Costly . . .
If you don’t understand the precision of your predictions, you can make a costly mistake! Regression models are a critical part of predictive analytics These models can help you make predictions in applied situations By entering values into the regression equation, you can predict the average outcome
- Limitations of Predictive Analytics: Lessons for Data Scientists
The evolving technology of Predictive Analytics is opening new possibilities for predicting future events by studying past performance Now that Big Data enables Data Scientists to review massive amounts of data, users can hope that the degree of accuracy in future predictions will only rise Yet, actual field tests reveal a different story
- eight tips for creating more accurate estimates | PMI
Estimating is an inherently imprecise and difficult process; this article discusses eight tips for creating more accurate estimates: 1) better estimates require better information; 2) never estimate alone, but always involve several people; 3) approximately right is better than absolutely wrong, so estimate in ranges rather than specific numbers; 4) use estimating methods that are workable
- Limits to prediction pre-read - Princeton University
But this heuristic can be applied to many other tasks for it is not intuitively obvious whether there are strong limits to prediction 3 Quantifying predictability: pitfalls and opportunities There is a long list of pitfalls in machine learning that may lead us to biased estimates — usually overesti-mates — of predictive accuracy
- On the unknowable limits to prediction | Nature Computational . . .
An outcome that is difficult to predict with one feature set may not be ‘unpredictable’ in an aleatoric sense: life trajectories may be trivial to predict in the future
- Aggregating predictions from experts: a review of statistical . . .
Aggregating expert predictions can outperform statistical ensembles when data is sparse, or rapidly evolving By making predictions, experts can gain insight into how forecasts are made, the assumptions implicit in forecasts, and ultimately how to best use the information forecasts provide to make critical decision about the future
- To be precise, the details don’t matter: On predictive . . .
As we argued elsewhere, there is a trade-off between making predictions that are very likely to be correct (due to their generality) but carry little relevant information, and predictions that allow for much information gain (due to their specificity) but are likely to be incorrect (Kwisthout Van Rooij, 2015)
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