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Are there any ML APIs or web services that accept a vector and run various regression scenarios to identify optimal fit? I suppose vectors for both training and testing would be required. Would gladly pay $1-$5 per batch for a service to do this.
* It allows you to model essentially arbitrary functions. The main model assumption is your choice of kernel, which defines the local correlation between nearby points.
* You can draw samples from the distribution of all possible functions that fit your data.
* You can quantify which regions of the function you have more or less certainty about.
* Imagine this situation: you want to discover the functional relationship between the inputs and outputs of a long-running process. You can test any input you want, but it's not practical to exhaustively grid-search the input space. A Gaussian Process model can tell you which inputs to test next so as to gain the most information, which makes it perfect for optimising complex simulations. Used in this way, it's one means of implementing "Bayesian Optimisation" [3]
[1] https://en.wikipedia.org/wiki/Gaussian_process
[2] http://scikit-learn.org/stable/modules/generated/sklearn.gau...
Earlier quoted context omitted.
I just want to provide the data and let a service decide the best algorithm. Weka and various ML tools require you select the algorithm and do the A/B testing on your own. There's an opportunity for an Optimizely of ML.
Model fishing is bad. You will find a model that looks good on your data. It will not be the model you should use.
The model I've consistently chosen (Decision Trees) may not be the best model. Need to get pushed outside my comfort zone.
I could put in the months/years like a proper Data Scientist and optimize the model. Or let a magic API tell me the best model. I'm lazy, so I prefer the latter ;-)
A tool that I've found myself reaching for more and more often is Gaussian Process Regression [1] [2] * It allows you to model essentially arbitrary functions. The main model assumption is your choice of kernel, which defines the local correlation between nearby points. * You can draw samples from the distribution of all possible functions that fit your data. * You can quantify which regions of the function you have…
Now this is a topic I desperately need. Can anyone here by any chance explain why would one choose predictors in multilinear regression that are NOT correlated to the target? I am having trouble understanding paper [1] where authors avoid using predictors that are correlated to target. Target is ozone concentration shown by referent instrument and predictors are low cost sensor outputs. [1] https://www.sciencedirect.…
A tool that I've found myself reaching for more and more often is Gaussian Process Regression [1] [2] * It allows you to model essentially arbitrary functions. The main model assumption is your choice of kernel, which defines the local correlation between nearby points. * You can draw samples from the distribution of all possible functions that fit your data. * You can quantify which regions of the function you have…
When I tried this to choose xgboost hyperparameters it didn't seem to perform much better than random search while also adding another layer of hyper-hyper-parameters.
In practice, I've found GPs to be great for getting actual insight into an unknown function, but much less useful as a black-box learner.
“model” isn’t a simple word.
> Assumptions of linear regression: There must be a linear relation between independent and dependent variables. That's not wrong, but it's a strong way to word it. If linear regression were only suitable when the variables were perfectly linearly related, it would get a lot less use. Practically, linear regression can be used when the relationship is linear-ish, at least in the interval of interest. In other words,…