Gradient Boosting is fitting a base learner $f_{i}(X)$ to the gradient of the loss function of an existing model $F_{i-1}(X)$ i.e. find base learner $f_i$ which minimises $L(-g_i, f_t(x_i))$ where $... View answer 2 votes I know this is an old question, but it took me a while to get my head around it The objective at each iteration of the gradient boosting algorithm is to find a base learner which gives the largest ... View answer 1 votes At a high level you need to find historical data matching what you need to forecast (average temperature) and any factors which influence it. So you need to be a domain expert, have access to one, or ... View answer 1 votes It's by definition, when you fit a guassian process you specify the mean function m(x) and the covariance function (or kernel) k(x,x'). Often the mean function is 0 and the covariance is the radial ... View answer 1 votes I don't think this is ususual, I experiance it quite frequently with regression problems. Generally I think it means the model is underspecified so instead of learning the actual relationship between ... View answer Accepted answer 1 votes I think what you're doing is correct, in fact it would be even more correct to introduce a gap between your test and train set, i.e. train <-head(stock_indicators,round(0.65*nrow(stock_indicators))... View answer 1 votes If you look at the 2nd equation under "Propagation function"$p_{j}(t)=\sum _{i=1}^No_{i}(t)w_{ij}+w_{0j}$The$w_{0j}$is the constant (bias), so it's reasonable to write that the$w_{ij}$weights ... View answer 1 votes The python hyperopt library will evaluate multiple trials in parallel, it's open source and there's a paper. Also I'm fairly sure AWS Sagemaker has a distributed Baysian algorithm, it doens't meet ... View answer 1 votes Speaking as someone from a finance background, the `usual' model for a stock price process is$\frac{dS}{S}=r dt + \sigma dW_t$i.e. we assume the returns (not the absolute price changes, i.e. dS/S ... View answer 0 votes I think I've figured out how to implement the algorithm described in the paper I'm studying. I suspect they used scipy.cluster.hierarchy. Anyway, my process is: Generate a distance matrix y from my ... View answer 0 votes The issue is your data violates the requirements of StratifiedShuffleSplit, specifically it's not possible to do a 70:30 split of the data and maintain the same number of distinct y values in the test ... View answer 0 votes The$\omega$in that section is a vector of weights not a single weight, and when they write$\omega_i$in this context they mean the weights for every connection and all the bias's at iteration$i\$ (...