Accuracy Values Missing with Ranger and classProbs = TRUE

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In this article, we will delve into a common issue in machine learning when using the ranger algorithm for classification problems. The problem is that all accuracy values are missing when classProbs is set to TRUE. We will explore possible solutions and provide step-by-step examples of how to fix this issue.

Background

The ranger algorithm is a popular choice for regression and classification tasks in R. It is an implementation of the random forest algorithm, which is known for its ability to handle high-dimensional data and non-linear relationships between variables.

In the context of classification problems, the classProbs parameter in ranger determines whether class probabilities are computed for each prediction. When set to TRUE, it returns a vector of class probabilities alongside the predicted class labels. However, this feature also has some limitations, as we will explore later.

Error Message

The error message provided by the ranger algorithm indicates that all accuracy metric values are missing when classProbs is set to TRUE. The exact error message reads:

Aggregating results
  Something is wrong; all the Accuracy metric values are missing:
      Accuracy       Kappa<br/>
   Min.   : NA   Min.   : NA<br/>
   1st Qu.: NA   1st Qu.: NA<br/>
   Median : NA   Median : NA<br/>
   Mean   :NaN   Mean   :NaN<br/>
   3rd Qu.: NA   3rd Qu.: NA<br/>
   Max.   : NA   Max.   : NA<br/>
   NA's   :2     NA's   :2<br/>
  ERROR: Stopping

This error message suggests that there is an issue with the classProbs parameter, but we will explore alternative solutions in the next section.

Solutions

Setting Method to “none”

According to the documentation for the caret package, setting the trainControl method to "none" can disable the parameter tuning grid feature. This might help resolve the issue with accuracy values missing when classProbs is set to TRUE.

Here’s an example of how to modify the code:

fit1 <- train(form = y ~.,
              x = X_train[, 2:3],
              y = factor(y_train),
              method = 'ranger',
              verbose = TRUE,
              trControl = trainControl(method = "none"),
              tuneGrid = expand.grid(mtry = 2, min.node.size = 1, splitrule = 'gini'),
              num.tree = 100, metric = 'Accuracy', importance = 'permutation')

Using Random Forest Methods Without Grid Search

The second answer on the Stack Exchange question suggests that random forest methods should not use complicated grids. This implies that using a simple grid search without tuning might resolve the issue.

Here’s an example of how to modify the code:

fit1 <- train(form = y ~.,
              x = X_train[, 2:3],
              y = factor(y_train),
              method = 'ranger',
              verbose = TRUE,
              tuneGrid = expand.grid(mtry = 2, min.node.size = 1, splitrule = 'gini'),
              num.tree = 100, metric = 'Accuracy', importance = 'permutation')

Note that we’ve removed the classProbs parameter from the train() function.

Adding Back Features

The original code had four features (V1, V2, V3, and V4) but only used two of them in the model. We can add back these missing features to see if it resolves the issue.

Here’s an example of how to modify the code:

fit1 <- train(form = y ~.,
              x = X_train[, 2:4],
              y = factor(y_train),
              method = 'ranger',
              verbose = TRUE,
              trControl = trainControl(method = "none"),
              tuneGrid = expand.grid(mtry = 2, min.node.size = 1, splitrule = 'gini'),
              num.tree = 100, metric = 'Accuracy', importance = 'permutation')

Conclusion

In this article, we explored a common issue in machine learning when using the ranger algorithm for classification problems. The problem is that all accuracy values are missing when classProbs is set to TRUE. We discussed possible solutions and provided step-by-step examples of how to fix this issue.

By setting the trainControl method to "none", using random forest methods without grid search, or adding back missing features, we can resolve the issue and get accurate accuracy values when classProbs is set to TRUE.

Last modified on 2024-12-18