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- using LightGBM
- estim = LightGBM.LGBMMulticlass(num_iterations=0, num_class=3, learning_rate=0.000000001)
- X = rand(1000,10);
- y = 1.0*(rand(1000) .> 0.5);
- y[1:3:end] = 2
- LightGBM.fit( estim, X, y; init_score = ones(3000), verbosity=10000)
- LightGBM.predict(estim, X)
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