- machine learning - Definition of Regressor - Cross Validated
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
- Log-Transforming target var for training a Random Forest Regressor
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
- What is the difference between Stochastic Regressor and Non-Stochastic . . .
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
- Should I choose Random Forest regressor or classifier?
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
- What are the differences between stochastic and fixed regressors in . . .
$\begingroup$ 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
- statistical significance - Why does an insignificant regressor become . . .
Adding a regressor can also change the numerator of the t-statistic, by changing the parameter estimate, due to dependence between regressors, which can move coefficients either toward or away from zero; it will also alter the denominator (so it's not as simple as just considering the numerator)
- Difference between regression and classification for random forest . . .
I am having a hard time completely understanding the difference between classification and regression for the three methods: Random forest, Gradient boosting and Neural networks (specifically Multi
- python - Monotone constraints in decision tree regressor or random . . .
I aim for a simple model that captures information from my dataset and holds true for monotone constraints, e g increasing water height leads to higher damage and increasing adaptation height leads to lower damage I came to the conclusion that a random forest or tree regressor would be a good option, as linear assumptions are violated
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