2018年6月25日 · Feature, independent variable, explanatory variable, regressor, covariate, or predictor are all names of the variables that are used to predict the target, outcome, dependent variable, regressand, or response. The terminology is ambiguous as it comes from different fields: statistics, econometrics, and machine learning.

2020年2月4日 · Log transforming the var gives is a normal-like distribution. When training a Random Forest regressor on the non-transformed var, I get worse performance than when I log-tranform the var. I am bit puzzled about whether I should do this knowning that the random forest regressor is predicting the mean of the leafs.

2016年4月28日 · For instance, in observational studies, such as pretty much all economics, you do not control the regressors. You can not set US GDP to a desired level, you can only observe it. Hence, in the model where GDP is a regressor, you want errors to be independent of GDP, because in this model you can only assume stochastic regressors.

$\begingroup$ What are the ramifications of this? > This has the basic implication that a sample with even one and varying deterministic regressor is no longer an identically distributed sample: 𝐸(𝑦𝑖)=𝑏𝐸(𝑥𝑖)+𝐸(𝑢𝑖) 𝐸(𝑦𝑖)=𝑏𝑥𝑖 and since the deterministic 𝑥𝑖 's are varying, it follows that the dependent variable does not have the same expected ...

2017年1月5日 · Whether you use a classifier or a regressor only depends on the kind of problem you are solving. You have a binary classification problem, so use the classifier. I could run randomforestregressor first and get back a set of estimated probabilities. NO. You don't get probabilities from regression.

2020年12月28日 · This switching of roles is what also happens in your image. Your curve gives an estimate of the expected true value conditional on the regressor. The true values (on the horizontal/x-axis) seem to be more or less evenly distributed around the predicted mean (you have to compare left/right for this and not up/down).

2015年2月24日 · Actually, scikit-learn does provide such a functionality, though it might be a bit tricky to implement. Here is a complete working example of such an average regressor built on top of three models.

2022年3月6日 · $\begingroup$ This question is off-topic here. The documentation could be clearer but external.forecasts must be a named list, and the future values of exogenous regressors in the mean equation must be in the mregfor element of the list (the ones in the variance equation go in the vregfor element).

2020年1月31日 · This strategy consists of fitting one regressor per target. Since each target is represented by exactly one regressor it is possible to gain knowledge about the target by inspecting its corresponding regressor. As MultiOutputRegressor fits one regressor per target it can not take advantage of correlations between targets.

2013年3月19日 · I start with my OLS regression: $$ y = \beta _0 + \beta_1x_1+\beta_2 D + \varepsilon $$ where D is a dummy variable, the estimates become different from zero with a low p-value.