7.1.1. mclearn.classifier.train_classifier¶
-
mclearn.classifier.
train_classifier
(data, feature_names, class_name, train_size, test_size, output='', random_state=None, coords=True, recall_maps=True, classifier=None, correct_baseline=None, balanced=True, returns=['correct_boolean', 'confusion_test'], report=True, pickle_path=None)[source]¶ Standard classifier routine.
Parameters: - data (DataFrame) – The DataFrame containing all the data.
- feature_names (array) – A list of column names in data that are used as features.
- class_name (str) – The column name of the target.
- train_size (int) – The size of the training set.
- test_size (int) – The size of the test set.
- output (str) – The name that will be attached to the path of the saved plots.
- random_state (int) – The value of the random state (used for reproducibility).
- coords (bool) – Whehter coordinates are part of the features.
- recall_maps (bool) – Wheter to make a map of recall scores.
- classifier (Classifier object) – An initialised scikit-learn Classifier object.
- correct_baseline (array) – If we want to compare our results to some baseline, supply the default predicted data here.
- balanced (bool) – Whether to make the training and test set balanced.
- returns (array) – The list of variables to be retuned by the function.
- report (bool) – Whether to print out the classification report.
- pickle_path (str) – If a pickle path is supplied, the classifier will be saved in the specified location.
Returns: - correct_boolean (array) – The boolean array indicating which test exmaples were correctly predicted.
- confusion_test (array) – The confusion matrix on the test examples.