human
5. Choosing the Best Model

Based on the experiments you performed earlier, in 1-2 paragraphs explain to the board of supervisors what single model you chose as the best model. Which model is generally the most appropriate based on the available data, limited resources, cost, and performance?
In 1-2 paragraphs explain to the board of supervisors in layman's terms how the final model chosen is supposed to work (for example if you chose a Decision Tree or Support Vector Machine, how does it make a prediction).
Fine-tune the model. Use Gridsearch with at least one important parameter tuned and with at least 3 settings. Use the entire training set for this.
What is the model's final F<sub>1</sub> score?
assistant
#Here is the model's final F<sub>1</sub> score.
y_train_numeric = y_train.replace('yes no'.split(), [1, 0])
y_test_numeric = y_test.replace('yes no'.split(), [1, 0])

class LRClassifier(object):
    """
    holds the LogisticRegression classifier
    """
    def __init__(self, c_range, score_function=f1_score, n_jobs=-1, folds=10,
                 training_features=X_train, training_targets=y_train_numeric,
                 test_features=X_test, test_targets=y_test_numeric):
        """
        :param:
         - `c_range`: range of 'C' values for grid search
         - `score_function`: function to maximize
         - `n_jobs`: number of parallel jobs for the grid search
         - `folds`: number of cross validation folds to use
         - `training_features`: array of training feature-data
         - `training_targets`: array of training target-values
         - `test_features`: array of testing feature-data
         - `test_targets`: array of testing target-data
        """
        self.c_range = c_range
        self.n_jobs = n_jobs
        self.folds = folds
        self.score_function = score_function
        self.training_features = training_features
        self.training_targets = training_targets
        self.test_features = test_features
        self.test_targets = test_targets
        
        self._scorer = None
        self._model = None
        self._grid = None
        self._parameters = None
        return

    @property
    def parameters(self):
        """
        :return: dict of grid search parameters
        """
        if self._parameters is None:
            self._parameters = {'penalty': ('l1', 'l2'),
                                'C': self.c_range}
        return self._parameters
    
    @property
    def scorer(self):
        """
        :return: scorer for the grid search
        """
        if self._scorer is None:
            self._scorer = make_scorer(self.score_function)
        return self._scorer

    @property
    def model(self):
        """
        :return: LogisticRegression object
        """
        if self._model...