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If you remove one tree from the random forest ensemble, then:

• Decrease in model accuracy: The overall quality of predictions may slightly deteriorate, as the number of voting trees decreases, reducing stability and generalization ability.
• Reduction in prediction time: The model will work faster, as fewer trees need to be traversed.
• Impact depends on the importance of the tree: If the removed tree was particularly informative (e.g., trained on a unique part of the data), the loss of accuracy will be more noticeable.

Random forest is based on the idea of averaging the predictions of many trees to reduce overfitting and increase stability. Removing a tree reduces the ensemble, which can lead to increased variance and decreased quality, but in most cases, the loss will be small, especially if there are many trees.

Example: if a random forest has 100 trees, removing one will reduce the count to 99, which practically will not affect the result, but if a significant part of the trees is removed, the model will become less reliable.