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This "silver" standard will serve as the basis for training a classification model, which will take as inputs features ("covariates") derived from the KB and output predicted positive and negative drug HOI associations.
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Signature descriptors were used as input features.
ML classification requires input features or dimensions.
The system adopts the acoustic signals as input features.
However, most classical regression methods take vectors as input features.
Such quality of input features is not diminished.
It predicts throughput based on multiple real-value input features.
The input features were the same as for the MLP.
Therefore, the application effects of different input features and data-mining classifiers are compared first.
In the experiments, the classification performances from various input features are evaluated.
For each classical method, different input features were compared to select the optimal feature set.
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