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I'm trying to use Accord.NET library for objects classification, but I failed to find any suitable examples and documentation is not enough to understand the process. My current code is

Predictor = new Boost<DecisionStump>();
AdaBoost<DecisionStump> Algo = new AdaBoost<DecisionStump>(Predictor, new ModelConstructor<DecisionStump>((double[] weights) => new DecisionStump(10)));
Algo.Run(set.X, set.Y);

It's written so just to be "compilable", but I have no idea what is the essence of the inputs parameter of DecisionStump and the whole second AdaBoost parameter. Could somebody explain, how to create and train Boost classifier properly?

J. Bond
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1 Answers1

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You have to give the input data and output labels for training, which you mentioned as set.X and set.Y. Typically it is by giving as parameters double[][] inputs and int[] outputs:

Algo.Run(inputs, outputs);
Frank
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