oddt.scoring.models package¶
Submodules¶
oddt.scoring.models.classifiers module¶
-
class
oddt.scoring.models.classifiers.neuralnetwork(*args, **kwargs)[source]¶ Bases:
oddt.scoring.models.classifiers.OddtClassifierAssemble Neural network or SVM using sklearn pipeline
Methods
score(descs, target_values)Return the mean accuracy on the given test data and labels.
fit
get_params
predict
predict_log_proba
predict_proba
set_params
-
oddt.scoring.models.classifiers.randomforest¶ alias of
sklearn.ensemble._forest.RandomForestClassifier
-
class
oddt.scoring.models.classifiers.svm(*args, **kwargs)[source]¶ Bases:
oddt.scoring.models.classifiers.OddtClassifierAssemble Neural network or SVM using sklearn pipeline
Methods
score(descs, target_values)Return the mean accuracy on the given test data and labels.
fit
get_params
predict
predict_log_proba
predict_proba
set_params
oddt.scoring.models.regressors module¶
Collection of regressors models
-
oddt.scoring.models.regressors.mlr¶ alias of
sklearn.linear_model._base.LinearRegression
-
class
oddt.scoring.models.regressors.neuralnetwork(*args, **kwargs)[source]¶ Bases:
oddt.scoring.models.regressors.OddtRegressorAssemble Neural network or SVM using sklearn pipeline
Methods
score(descs, target_values)Return the coefficient of determination \(R^2\) of the prediction.
fit
get_params
predict
set_params
-
oddt.scoring.models.regressors.pls¶ alias of
sklearn.cross_decomposition._pls.PLSRegression
-
oddt.scoring.models.regressors.randomforest¶ alias of
sklearn.ensemble._forest.RandomForestRegressor
-
class
oddt.scoring.models.regressors.svm(*args, **kwargs)[source]¶ Bases:
oddt.scoring.models.regressors.OddtRegressorAssemble Neural network or SVM using sklearn pipeline
Methods
score(descs, target_values)Return the coefficient of determination \(R^2\) of the prediction.
fit
get_params
predict
set_params