robustx.lib.models.sklearn_models package
Submodules
robustx.lib.models.sklearn_models.DecisionTreeModel module
- class robustx.lib.models.sklearn_models.DecisionTreeModel.DecisionTreeModel[source]
Bases:
SklearnModel
A Decision Tree Classifier model wrapper for scikit-learn.
Inherits from SklearnModel and initializes a DecisionTreeClassifier as the underlying model.
robustx.lib.models.sklearn_models.LogisticRegressionModel module
- class robustx.lib.models.sklearn_models.LogisticRegressionModel.LogisticRegressionModel[source]
Bases:
SklearnModel
A Logistic Regression Classifier model wrapper for scikit-learn.
Inherits from SklearnModel and initializes LogisticRegression as the underlying model.
robustx.lib.models.sklearn_models.SVMModel module
- class robustx.lib.models.sklearn_models.SVMModel.SVMModel[source]
Bases:
SklearnModel
A SVM model wrapper for scikit-learn.
Inherits from SklearnModel and initializes SVC as the underlying model.
robustx.lib.models.sklearn_models.SklearnModel module
- class robustx.lib.models.sklearn_models.SklearnModel.SklearnModel(model)[source]
Bases:
BaseModel
A base class for scikit-learn models.
This class wraps a scikit-learn model and provides methods for training, predicting, and evaluating the model. Inherits from BaseModel.
- evaluate(X, y)[source]
Evaluates the model’s performance using accuracy and F1 score.
@param X: The feature variables, should be a DataFrame. @param y: The true target values, should be a DataFrame. @return: A dictionary with “accuracy” and “f1_score” of the model.
- Return type:
dict
- predict(X)[source]
Predicts the outcomes for given feature variables.
@param X: The feature variables, should be a DataFrame. @return: Predictions for each instance, returned as a DataFrame.
- Return type:
DataFrame
- predict_proba(X)[source]
Predicts the probabilities of outcomes for given feature variables.
@param X: The feature variables, should be a DataFrame. @return: Probabilities of each outcome, returned as a DataFrame.
- Return type:
DataFrame
- predict_proba_tensor(X)[source]
Predicts the probabilities of outcomes for given feature variables.
@param X: The feature variables, should be a DataFrame. @return: Probabilities of each outcome, returned as a DataFrame.
- Return type:
Tensor