# SklearnClassifier **`class SklearnClassifier(config)`** Bases: [`Classifier`](squirro.lib.nlp.steps.classifiers.Classifier.md#squirro.lib.nlp.steps.classifiers.Classifier) The sklearn [`Classifier`](squirro.lib.nlp.steps.classifiers.Classifier.md#squirro.lib.nlp.steps.classifiers.Classifier) is a generic [scikit-learn](http://scikit-learn.org/stable/index.html) classifier. **Note - currently we support following scikit-learn classifier:** - [MultinomialNB](https://scikit-learn.org/stable/modules/naive_bayes.html#multinomial-naive-bayes) - [BernoulliNB](https://scikit-learn.org/stable/modules/naive_bayes.html#bernoulli-naive-bayes) - [GaussianNB](https://scikit-learn.org/stable/modules/naive_bayes.html#gaussian-naive-bayes) - [Perceptron](https://scikit-learn.org/stable/modules/linear_model.html#perceptron) - [SGDClassifier](https://scikit-learn.org/stable/modules/linear_model.html#stochastic-gradient-descent-sgd) - [MLPClassifier](https://scikit-learn.org/stable/modules/neural_networks_supervised.html#multi-layer-perceptron) - [PassiveAggressiveClassifier](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html#sklearn.linear_model.PassiveAggressiveClassifier) - [PassiveAggressiveRegressor](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveRegressor.html#sklearn.linear_model.PassiveAggressiveRegressor) - [SVC](https://scikit-learn.org/stable/modules/svm.html#classification) - [SVR](https://scikit-learn.org/stable/modules/svm.html#regression) **Input** - the input field need to be of type [`list`](https://docs.python.org/3.11/library/stdtypes.html#list) [ [`float`](https://docs.python.org/3.11/library/functions.html#float) or [`int`](https://docs.python.org/3.11/library/functions.html#int) ] or `numpy.ndarray`. In addition also the types [`str`](https://docs.python.org/3.11/library/stdtypes.html#str), [`float`](https://docs.python.org/3.11/library/functions.html#float), [`int`](https://docs.python.org/3.11/library/functions.html#int), [`list`](https://docs.python.org/3.11/library/stdtypes.html#list) [ [`str`](https://docs.python.org/3.11/library/stdtypes.html#str) ] and `numpy.sparse` matrices are supported. **Output** - the output field is filled with data of type [`str`](https://docs.python.org/3.11/library/stdtypes.html#str) or [`dict`](https://docs.python.org/3.11/library/stdtypes.html#dict) { [`str`](https://docs.python.org/3.11/library/stdtypes.html#str): [`float`](https://docs.python.org/3.11/library/functions.html#float) } if the model has the property “predict_proba”. The key of the [`dict`](https://docs.python.org/3.11/library/stdtypes.html#dict) is the predicted class name and the value is the probability/confidence returned by the model. Parameters - `type` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)) – sklearn - `model_type` ([`str`](https://docs.python.org/3.11/library/stdtypes.html#str)) – Type of scikit learn model - `model_kwargs` ([`dict`](https://docs.python.org/3.11/library/stdtypes.html#dict)`, ``{}`) – Keyword arguments for scikit learn model - `use_sparse` ([`bool`](https://docs.python.org/3.11/library/functions.html#bool)`, ``False`) – Whether or not to use a sparse input matrix **Example** ```json { "step": "classifier", "type": "sklearn", "model_type": "GaussianNB", "model_kwargs": {}, "label_field": "label", "input_fields": ["embedded_extract"], "output_field": "prediction", } ``` Methods SummaryMethods Documentation **`load()`** Load a step **`process_batch(batch)`** Process a batch of documents. If not defined will default to using self.process_doc for each document in the batch. Parameters `batch` ([`list`](https://docs.python.org/3.11/library/stdtypes.html#list)`(`[`Document`](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)`)`) – List of documents Returns List of processed documents Return type [list](https://docs.python.org/3.11/library/stdtypes.html#list)([Document](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)) **`save()`** Save a step **`train(docs)`** Train on a step of a set of documents Parameters `docs` (`generator``(`[`Document`](../technical/libnlp/base.md#squirro.lib.nlp.document.Document)`)`) – Generator of documents Returns Generator of processed documents Return type generator([Document](../technical/libnlp/base.md#squirro.lib.nlp.document.Document))