You work as a machine learning specialist for a retail marketing firm. You are responsible for the machine learning models used for product marketing. Your latest assignment has you building a model to predict whether or not a particular marketing campaign will benefit from social media advertising. You have gathered your social media and product marketing data and selected your model algorithm. You are now in the process of evaluating your model. You are using the scikit-learn model_selection package in a pipeline using cross validation to select the best performing model. When setting the parameters for your scoring your cross validation runs, which scoring technique should you use?

Answer options:

A.f1
B.adjusted_mutual_info_score
C.rand_score
D.completeness_score

Correct Answer: A
Option A is correct. The f1 scoring metric is used for binary targets. Your target is binary: predict whether or not a particular marketing campaign will benefit from social media advertising.
Option B is incorrect. The adjusted_mutual_info_score metric is used in clustering problems. You are not solving a clustering problem.
Option C is incorrect. The rand_score metric is used in clustering problems. You are not solving a clustering problem.
Option D is incorrect. The completeness_score metric is used in clustering problems. You are not solving a clustering problem.
Reference:
Please see the Kaggle article titled Cross-Validation (https://www.kaggle.com/alexisbcook/cross-validation), the Scikit-learn page titled 3.3. Metrics and scoring: quantifying the quality of predictions (https://scikit-learn.org/stable/modules/model_evaluation.html), the Scikit-learn page titled sklearn.metrics.adjusted_mutual_info_score (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.adjusted_mutual_info_score.html#sklearn.metrics.adjusted_mutual_info_score), the Scikit-learn page titled sklearn.metrics.rand_score (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.rand_score.html#sklearn.metrics.rand_score), the Scikit-learn page titled sklearn.metrics.completeness_score (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.completeness_score.html#sklearn.metrics.completeness_score), the Scikit-learn page titled sklearn.metrics.f1_score (https://scikit-learn.org/stable/modules/generated/sklearn.metrics.f1_score.html#sklearn.metrics.f1_score)