mlr3proba | Probabilistic Supervised Learning for mlr3 | Machine Learning library
kandi X-RAY | mlr3proba Summary
kandi X-RAY | mlr3proba Summary
The current mlr3proba release focuses on survival analysis, and contains:.
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Lines of Code : 9License : Weak Copyleft (LGPL-3.0)
Copy@Article{,
title = {mlr3proba: An R Package for Machine Learning in Survival Analysis},
author = {Raphael Sonabend and Franz J Király and Andreas Bender and Bernd Bischl and Michel Lang},
journal = {Bioinformatics},
month = {02},
year = {20 install.packages("mlr3proba")
remotes::install_github("mlr-org/mlr3proba")
Community Discussions
Trending Discussions on mlr3proba
QUESTION
I am a beginner on mlr3 and am facing problems while running AutoFSelector learner associated to glmnet on a classification task containing >2000 numeric variables. I reproduce this error while using the simpler mlr3 predefined task Sonar. For note, I am using R version 4.1.2 (2021-11-01)on macOS Monterey 12.1. All required packages have been loaded on CRAN.
...ANSWER
Answered 2022-Jan-24 at 18:05This is a problem specific to glmnet. glmnet requires at least two features to fit a model, but in at least one configuration (the first ones in a sequential forward search) you only have one feature.
There are two possibilities to solve this:
- Open an issue in mlr3fselect and request a new argument
min_features(there already ismax_features) to be able to start the search with 2 or more features. - Augment the base learner with a fallback which gets fitted if the base learner fails. Here is fallback to a simple logistic regression:
QUESTION
when I run the code below for training a model in mlr3proba after encoding and scaling my dataset with mlr3pipeline:
...ANSWER
Answered 2021-Apr-30 at 15:21You need to wrap the learner in the GraphLearner PipeOp:
QUESTION
I am using mlr3proba package for machine learning survival analysis.
My dataset contains factor, numeric and integer features.
I used 'scale' and 'encode' pipeops to preprocess my dataset for deephit and deepsurv neural network methods as following codes:
ANSWER
Answered 2021-Apr-26 at 07:15Hi thanks for using mlr3proba! The reason for this is because the parameter names change when wrapped in the pipeline, you can see this in the example below. There are a few options to solve this, you could change the parameter ids to match the new names after wrapping in PipeOps (Option 1 below), or you could specify the tuning ranges for the learner first then wrap it in the PipeOp (Option 2 below), or you could use an AutoTuner and wrap this in the PipeOps. I use the final option in this tutorial.
QUESTION
For survival analysis, I am using mlr3proba package of R.
My dataset consists of 39 features(both continuous and factor, which i converted all to integer and numeric) and target (time & status).
I want to tune hyperparameter: num_nodes, in Param_set.
This is a ParamUty class parameter with default value: 32,32.
so I decided to transform it.
I wrote the code as follows for hyperparamters optimization of surv.deephit learner using 'nested cross-validation' (with 10 inner and 3 outer folds).
ANSWER
Answered 2021-Apr-17 at 08:46Hi thanks for using mlr3proba. I have actually just finished writing a tutorial that answers exactly this question! It covers training, tuning, and evaluating the neural networks in mlr3proba. For your specific question, the relevant part of the tutorial is this:
QUESTION
I am using R version 3.6.3, mlr3 version 0.3.0 and mlr3proba version 0.1.6 (the latest development versions I could find) and xgboost version 0.90.0.2 → I am trying to use the command: lrn("surv.xgboost")
but I receive the error message:
ANSWER
Answered 2020-Jul-16 at 10:58Thanks for your interest in mlr3proba :) The xgboost learner is currently in limbo between mlr3proba and mlr3learners, as of tomorrow you can simply run
QUESTION
R version used: 3.6.3, mlr3 version: 0.4.0-9000, mlr3proba version: 0.1.6.9000, mlr3pipelines version: 0.1.2 and xgboost version: 0.90.0.2 (as stated on Rstudio package manager)
I have deployed the following graph pipeline:
...ANSWER
Answered 2020-Jul-29 at 08:51The problem lies in distr6 here, please install the latest versions of distr6 (1.4.2) and mlr3proba (0.2.0) from CRAN and then try again.
QUESTION
R version used: 3.6.3, mlr3 version: 0.4.0-9000, mlr3proba version: 0.1.6.9000 and xgboost version: 0.90.0.2 (as stated on Rstudio package manager)
ANSWER
Answered 2020-Jul-24 at 18:55Hi thanks for using mlr3proba! Good spot on the documentation problem, I will get that fixed asap. xgboost does not natively predict distr, this is a mistake in the documentation. You can check this with LearnerSurvXgboost$new()$predict_types. However it is easy to get a distribution prediction:
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