Is data-driven modelling and machine learning the same thing?
2021年1月27日 · When people use data-driven modeling and machine learning interchangeably, they normally intend for them to mean the same thing. Simple definitions might be like this: …
When should you remove Outliers - Cross Validated
2021年7月6日 · Then, once you get a value in this training data you have to use the obtained values on the testing data. And yes - simple things like centering the data by subtracting a …
regression - Is it wrong to remove outliers from dependent …
2021年9月1日 · $\begingroup$ Removing outliers based on the data alone (including boxplots) will invalidate standard theory, which does not take into account that the dataset to be …
On the difference between parameter driven models and …
2017年2月12日 · An example of an observation-driven model could be the standard GARCH model. An example of a parameter-driven model could be the Beta-t-EGARCH model (Harvey …
Practical thoughts on explanatory vs. predictive modeling
In explanation (read "causal explanation") there is actually a large focus on theory and domain knowledge and the data are used to test these assumptions/theories. In contrast, in prediction …
How to split the dataset for cross validation, learning curve, and ...
2014年5月1日 · Nested validation is of utmost importance for data-driven optimization, and cross validation is a very powerful approaches (particularly if iterated/repeated). Then, whether …
What exactly is a Bayesian model? - Cross Validated
2014年12月14日 · A statistical model can be seen as a procedure/story describing how some data came to be. A Bayesian model is a statistical model where you use probability to …
Choosing variables to include in a multiple linear regression model
With modern computing power it is feasible to fit all 2^10 = 1024 possible combinations of explanatory variables and choose the best model by one of a number of possible criteria eg …
When is unbalanced data really a problem in Machine Learning?
Unbalanced data is only a problem depending on your application. If for example your data indicates that A happens 99.99% of the time and 0.01% of the time B happens and you try to …
machine learning - Differences between prior distribution and …
2019年2月27日 · Say you are interested in modeling the variation in weight of elephants, and your prior distribution leads to a prior predictive with substantial probability over 20 tons. Then you …