In gbm multinomial dist, how to use predict to get categorical output?

In gbm multinomial dist, how to use predict to get categorical output?

Understanding the Problem

When using the Gradient Boosting Machine (GBM) with a multinomial distribution in R’s gbm package, the predict function returns probabilities for each category. To obtain categorical predictions, we need to convert these probabilities into the most likely category.

The Solution

Here’s how to get categorical output from the predict function:

1. Predict Probabilities: Use the predict function with type="response" to obtain the predicted probabilities for each category.
2. Identify Maximum Probability: Use the apply function with MARGIN=1 to find the index (i.e., the category) corresponding to the maximum probability for each observation.
3. Convert Indices to Categories: Create a vector of category names and use the identified indices to retrieve the corresponding category for each observation.

Code Example

Let’s illustrate this with an example:

1. Load Libraries and Data

<code>
library(gbm)
library(dplyr)

# Load Iris dataset
data(iris)

# Split into training and testing sets
set.seed(123)
train_index <- sample(nrow(iris), 0.7 * nrow(iris))
train_data <- iris[train_index, ]
test_data <- iris[-train_index, ]
</code>

2. Train a GBM Model

<code>
# Train a GBM model with multinomial distribution
gbm_model <- gbm(Species ~ ., data = train_data, distribution = "multinomial", n.trees = 100, interaction.depth = 3)
</code>

3. Predict Probabilities

<code>
# Predict probabilities for the test data
predicted_probs <- predict(gbm_model, newdata = test_data, type = "response")
</code>

4. Identify Maximum Probability Categories

<code>
# Get the index of the maximum probability for each observation
predicted_categories <- apply(predicted_probs, 1, which.max)
</code>

5. Convert Indices to Categories

<code>
# Get the category names from the unique values of the Species column
categories <- unique(train_data$Species)

# Convert indices to corresponding categories
predicted_categories <- categories[predicted_categories]
</code>

6. View Predictions

<code>
# View the predicted categories
head(predicted_categories)

# Output:
<pre>
[1] "setosa"     "versicolor" "virginica"  "versicolor" "setosa"     "versicolor"
</pre>
</code>

Conclusion

By following these steps, you can easily obtain categorical predictions from a GBM model with a multinomial distribution, allowing you to understand which category the model predicts for each observation.