Error Prediction with mlr: A Case Study

Introduction

Error prediction is a crucial aspect of machine learning, as it allows us to forecast and mitigate potential errors in our models. In this article, we’ll delve into the world of error prediction using the mlr package in R. We’ll explore the common issues that can arise when trying to make predictions with mlr, and provide step-by-step guidance on how to overcome them.

Background

The mlr package is an implementation of the Machine Learning Repository (MLR) framework, which provides a unified interface for various machine learning algorithms. The mlr package includes tools for data preparation, model selection, and hyperparameter tuning, making it an excellent choice for building robust and accurate models.

However, when working with mlr, users often encounter errors that can be frustrating to resolve. In this article, we’ll focus on one such error that arises when trying to make predictions with the classif.h2o.deeplearning learner.

The Error

Let’s take a look at the code snippet provided in the question:

library(mlr)
a <- data.frame(y = factor(c(1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0)),
                x1 = rep(c("a", "b", "c"), times = c(6, 3, 3)))
aTask <- makeClassifTask(data = a, target = "y", positive = "1")
h2oLearner <- makeLearner("classif.h2o.deeplearning",
                          predict.type = "prob")
model <- train(h2oLearner, aTask)

b <- data.frame(x1 = rep(c("a", "b", "c"), times = c(3, 5, 4)))
pred <- predict(model, newdata = b)

When we run this code, we encounter an error:

Error in checkPredictLearnerOutput(.learner, .model, p) : predictLearner for classif.h2o.deeplearning has returned not the class levels as column names: p0, p1

Understanding the Error

The error message indicates that the predict function is returning a vector of probabilities (p0 and p1) instead of the predicted classes. This can be confusing, especially when we’re used to seeing class labels (e.g., “a”, “b”, or “c”).

To understand why this happens, let’s take a closer look at the classif.h2o.deeplearning learner.

The classif.h2o.deeplearning Learner

The classif.h2o.deeplearning learner is a type of neural network designed for classification tasks. When using this learner, we need to specify the predict.type parameter, which determines how the model will make predictions.

By default, the predict.type parameter is set to "response", which means the model will return the predicted response (i.e., the probability of each class). However, in our case, we want to predict probabilities for specific classes.

Fixing the Error

To fix this error, we can simply change the predict.type parameter to "prob". Here’s the corrected code:

library(mlr)
a <- data.frame(y = factor(c(1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 1, 0)),
                x1 = rep(c("a", "b", "c"), times = c(6, 3, 3)))
aTask <- makeClassifTask(data = a, target = "y", positive = "1")
h2oLearner <- makeLearner("classif.h2o.deeplearning",
                          predict.type = "prob") # Change predict.type to "prob"
model <- train(h2oLearner, aTask)

b <- data.frame(x1 = rep(c("a", "b", "c"), times = c(3, 5, 4)))
pred <- predict(model, newdata = b)

By setting predict.type to "prob", we ensure that the model returns probabilities for each class. This should fix the error and give us the desired output.

Additional Tips

While we’ve resolved the specific issue mentioned in the question, there are some additional tips to keep in mind when working with mlr:

• Always check the documentation for the specific learner you’re using, as different learners may have varying requirements.
• Make sure to validate your data before training a model. In our example, we assumed that the y variable was a factor, but if it’s not, this could lead to errors.
• Consider using cross-validation when tuning hyperparameters to avoid overfitting.

Conclusion

Error prediction is an essential aspect of machine learning, and understanding how to overcome common issues like the one described in this article can greatly improve your workflow. By following these tips and best practices, you’ll be better equipped to handle errors and build more accurate models with mlr.

Last modified on 2023-06-07