Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for customizable machine learning models. In some implementations, data is received, including (i) example data sets and (ii) data specifying one or more criteria to be assessed. A set of multiple models is trained, where each model in the set of models is trained using a training data set comprising a different subset of the example data sets. Output of the models is obtained for various example data sets, and a combination of n-grams is selected based on the outputs. The example data sets are used to train a classifier to evaluate input data with respect to the specified one or more criteria based on whether the input data includes the n-grams in the selected combination of n-grams.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for customizable machine learning models. In some implementations, data is received, including (i) example data sets and (ii) data specifying one or more criteria to be assessed. A set of multiple models is trained, where each model in the set of models is trained using a training data set comprising a different subset of the example data sets. Output of the models is obtained for various example data sets, and a combination of n-grams is selected based on the outputs. The example data sets are used to train a classifier to evaluate input data with respect to the specified one or more criteria based on whether the input data includes the n-grams in the selected combination of n-grams.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for customizable machine learning models. In some implementations, data is received, including (i) example data sets and (ii) data specifying one or more criteria to be assessed. A set of multiple models is trained, where each model in the set of models is trained using a training data set comprising a different subset of the example data sets. Output of the models is obtained for various example data sets, and a combination of n-grams is selected based on the outputs. The example data sets are used to train a classifier to evaluate input data with respect to the specified one or more criteria based on whether the input data includes the n-grams in the selected combination of n-grams.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for customizable machine learning models. In some implementations, data is received, including (i) example data sets and (ii) data specifying one or more criteria to be assessed. A set of multiple models is trained, where each model in the set of models is trained using a training data set comprising a different subset of the example data sets. Output of the models is obtained for various example data sets, and a combination of n-grams is selected based on the outputs. The example data sets are used to train a classifier to evaluate input data with respect to the specified one or more criteria based on whether the input data includes the n-grams in the selected combination of n-grams.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for customizable machine learning models. In some implementations, data is received, including (i) example data sets and (ii) data specifying one or more criteria to be assessed. A set of multiple models is trained, where each model in the set of models is trained using a training data set comprising a different subset of the example data sets. Output of the models is obtained for various example data sets, and a combination of n-grams is selected based on the outputs. The example data sets are used to train a classifier to evaluate input data with respect to the specified one or more criteria based on whether the input data includes the n-grams in the selected combination of n-grams.