To clarify: You have 2.3M samples How many features? How many active features on average per sample? In 7k classes: multiclass or multilabel?
Have you tried limiting the depth of the forest? Have you tried embedding your feature space into a smaller vector (pre-trained embeddings, hashing, lda, PCA or random projection)?
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