dsci_310_group_11_pkg.optimize
Module Contents
Functions
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DESCRIPTION: Maximizes model accuracy based on tuning hyperparameters of each model (respective if statements) |
- dsci_310_group_11_pkg.optimize.hp_optimizer(model, X_train, y_train)
DESCRIPTION: Maximizes model accuracy based on tuning hyperparameters of each model (respective if statements)
ACTION: Loops over chosen hyperparemeters in a the scores_dict/param_grid dictionary and appends the mean cross validation score of each hyperparameter to the scores_dict dictionary
- INPUTS: model - an input string, referencing the specific model that the function should run.
X_train - a dataframe object containing prediction features. y_train - a series object containing target variables.
RETURNS: Dataframe containing the mean cross validation scores of each hyperparameter value
TODO: Modularize param_grid values
Logistic Regression model needs to optimize the “C” value (a value of model regularization) C: Regularization; penalty of wrongly classified examples Creates a simple ML pipeline that scales the data and applies LogisticRegression