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//! set and then aggregate their individual predictions to form a final prediction. In classification setting the overall prediction is the most commonly
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//! set and then aggregate their individual predictions to form a final prediction. In classification setting the overall prediction is the most commonly
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//! occurring majority class among the individual predictions.
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//! occurring majority class among the individual predictions.
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//!
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//!
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//! In smartcore you will find implementation of RandomForest - a popular averaging algorithms based on randomized [decision trees](../tree/index.html).
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//! In `smartcore` you will find implementation of RandomForest - a popular averaging algorithms based on randomized [decision trees](../tree/index.html).
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//! Random forests provide an improvement over bagged trees by way of a small tweak that decorrelates the trees. As in bagging, we build a number of
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//! Random forests provide an improvement over bagged trees by way of a small tweak that decorrelates the trees. As in bagging, we build a number of
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//! decision trees on bootstrapped training samples. But when building these decision trees, each time a split in a tree is considered,
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//! decision trees on bootstrapped training samples. But when building these decision trees, each time a split in a tree is considered,
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//! a random sample of _m_ predictors is chosen as split candidates from the full set of _p_ predictors.
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//! a random sample of _m_ predictors is chosen as split candidates from the full set of _p_ predictors.
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