Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [mlr]

"mlr" is an R Package focussing on machine learning. The abbreviation "mlr" stands for "machine learning in R"

The mlr package provides many machine learning methods. It is similar to the caret package, but provides more specialized procedures such as multi criteria evaluation and optimization or partial dependency plots.

For a more detailed insight visit the Vignette of the package mlr.

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mlr compared to caret

I’ve been using mlr a little to learn about machine learning, but recently found out about caret. The way I understand it is that both are wrappers to various ML packages, but have slightly different approaches. Although mlr appears to also wrap…
Mooks
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Multiple Linear Regression with more variables than samples

I'm currently learning chemometrics for my work and I have a simple question about Multiple Linear Regression (MLR). Just to explain the context: I am simply using UV-Vis-NIR spectra (2500 wavelengths) to quantify a molecule in presence of…
Snedron
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Parameter 'C' cannot be optimized for 'nu-svr'? mlr3 with kernlab

I am trying to optimize an SVR model within the mlr3 ecosystem with the kernlab package and I am getting the following error: The parameter 'C' can only be set if the following condition is met 'type {eps-svr, eps-bsvr}'. Instead the…
Eduardo A. Sánchez Torres
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R alternative to scikit-learn

As a statics researcher, I've been using R since university and I know it quite well, I also know that it's immediate, but it quickly gets chaotic, and this also happens because of the variety and inconsistency of the packages and object types. I've…
carlo
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Making a residual plot in multiple linear regression

I need to make a residual plot and I was wondering whether I make the plots in multiple linear regression on one independent variable at a time (like making a simple linear regression) or the all of the ten independent variables at the same time…
Olivia Heino
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How do I fit models with predetermined covariates?

I'm trying to fit a multiple linear regression model. It has 10 variables, 2 of which are specified (e.g. $\beta_4 = 0.5$, $\beta_7 = 0.77$). How do I go about fitting this in R? I need to find the intercept (estimate) of the model as well.
vk1233
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Multiple regression results help

For my first ever research paper I've run a hierarchal multiple linear regression with two predictors and one outcome variable, however I don't understand my results. I've found predictor A to be a significant predictor for my outcome variable…
ummmm
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Error "Feature names stored in `object` and `newdata` are different!" using xgboost in mlr package

I am trying to make a multilabel classification model for XGBoost. I have one that works for RF, but when I try this code below for XGBoost I get the error: "Error in predict.xgb.Booster(m, newdata = data.matrix(.newdata), ...) : Feature names…
agnesg2g
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Why does the accuracy of leave-one-out CV change between runs for my kNN task?

I'm getting into ML, working through the book Machine Learning with R, the Tidyverse and MLR. Early on the concept of cross validation is introduced as a means to gauge the ability of my model to work with previously unseen data. Specifically,…
korolev
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R mlr - How does tuneThreshold work?

I would like to tune the threshold for the following classification task using tuneThreshold in conjunction with a learner parameter. I first tried to tune the threshold during the tuning of the learner by setting makeTuneControlRandom(...,…
user51462
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Predicting house pricing using MLR

My problem I want to predict housing prices in a city (for an upcoming year). My solution Create a MLR, where average housing price is dependent and macroeconomic fundamentals (population, gdp, household income etc.) are independent. How do I…
Jan Vo
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Poor model fit - Difference between SEM and MLR

For a study I am researching a quite simple research model (7 IVs - 1 DV), in which I am not interested in underlying relations between the IVs: the relation between the IVs and the DV is all that matters. My sample size exists of n = 189. The data…
B. van der Wal
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Model assumption to evalute and graph used to evalute each assumption

When I have to fit a MLR model of bwt on 3 variables (lwt, age, smoke) , before evaluating the model, including at the overall fit using R^2 adju. and looking at the interval estimates of their model parameters, what model of assumptions are best to…
user340768
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Difference in resample MSE from mlr3

I created a new task with TaskRegr$new, a learner with lrn('regr.ranger'), a search space with ps, fed those to an AutoTuner$new and then ran resample using resampling=rsmp('cv', folds=4) So that looks like rr1 <- resample(task=task_allcols,…
Dean MacGregor
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Is a classification comparison between Linear Discriminant Analysis and a SVM trained on said linear discriminants appropriate?

I am currently trying to investigate the classification accuracy of two models on a wide dataset (79*222), with 4 balanced classes. The models are: Principal Component Analysis, Linear Discriminant Analysis (For reduction and…
wrace
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