Allianz Financial Services (AFS) is a banking group offering end-to-end banking and financial solutions in South East Asia through its consumer banking, business banking, Islamic banking, investment finance and stock broking businesses as well as unit trust and asset administration, having served the financial community over the past five decades. AFS being one the largest banks in the region is planning to improve its segment business by launching a campaign to identify potential customers for various new products launched based on their past behavior? AFS is looking for both batch and real-time predictive analytics AFS is running a campaign to improve the revenue by recommending new products to customer and upscale the business.
AFS depend on ML to advise recommendations of new products that would suit a specific customer based on his previous history.
Select 3 options.
Click on the arrows to vote for the correct answer
A. B. C. D. E. F. G.Answer : B,E, F.
Option A is incorrect -ML models for binary classification problems predict a binary outcome.
https://docs.aws.amazon.com/machine-learning/latest/dg/types-of-ml-models.htmlOption B is correct -ML models for multiclass classification problems allow you to generate predictions for multiple classes (predict one of more than two outcomes).
https://docs.aws.amazon.com/machine-learning/latest/dg/types-of-ml-models.htmlOption C is incorrect -ML models for regression problems predict a numeric value.
https://docs.aws.amazon.com/machine-learning/latest/dg/types-of-ml-models.htmlOption D is incorrect -Amazon ML provides an industry-standard accuracy metric for binary classification models called Area Under the (Receiver Operating Characteristic) Curve (AUC).
https://docs.aws.amazon.com/machine-learning/latest/dg/binary-model-insights.htmlOption E is correct -Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data and evaluating them on the complementary subset of the data.
Use cross-validation to detect overfitting.
https://docs.aws.amazon.com/machine-learning/latest/dg/cross-validation.htmlOption F is correct -The macro-average F1 score is used to evaluate the predictive accuracy of a multiclass metric.
https://docs.aws.amazon.com/machine-learning/latest/dg/multiclass-model-insights.htmlOption G is incorrect -For linear regression tasks, Amazon ML uses the industry standard root mean square error (RMSE) metric.
https://docs.aws.amazon.com/machine-learning/latest/dg/regression-model-insights.htmlThe correct options for this scenario are A, B, and D.
A. Amazon ML uses logistic regression algorithm through binary classification to solve the business problem: Binary classification is used to classify data into two classes. In this case, the algorithm is used to identify potential customers for new products. Therefore, logistic regression algorithm through binary classification would be appropriate.
B. Amazon ML uses multi-nominal logistic regression algorithm through multi-class classification to solve the business problem: Multi-class classification is used when there are more than two classes to classify data. In this case, there are multiple new products launched, and the algorithm needs to recommend them to customers. Therefore, multi-nominal logistic regression algorithm through multi-class classification would be appropriate.
C. Amazon ML uses linear regression algorithm through regression model to solve the business problem: Linear regression algorithm through regression model is used when the target variable is continuous. In this case, the algorithm is used to recommend new products to customers, which is a classification problem, not a regression problem. Therefore, this option is not correct.
D. Amazon ML uses Area Under the (Receiver Operating Characteristic) Curve (AUC) to provide accuracy of the model: AUC is a metric used to evaluate the performance of binary classification models. It measures the ability of the model to distinguish between positive and negative classes. In this case, the algorithm is used for binary classification to identify potential customers for new products. Therefore, using AUC to evaluate the model's accuracy would be appropriate.
E. Cross-validation is a technique for evaluating ML models by training several ML models on subsets of the available input data to detect overfitting which eventually fails to generalize the pattern: Cross-validation is a technique used to evaluate the performance of the model by training and testing the model on different subsets of the available data. This technique is used to avoid overfitting, which occurs when the model is too complex and fits the training data too closely. Although cross-validation is an important technique, it is not relevant to the question asked.
F. Amazon ML uses macro-average F1 score to provide accuracy of the model: F1 score is a metric used to evaluate the performance of the model in classification tasks. It is the harmonic mean of precision and recall. Macro-average F1 score is calculated by taking the average of the F1 score for each class. In this case, the algorithm is used for multi-class classification to recommend new products to customers. Therefore, macro-average F1 score is not relevant to the question asked.
G. Amazon ML uses standard root mean square error (RMSE) metric to provide accuracy of the model: RMSE is a metric used to evaluate the performance of the model in regression tasks. It measures the difference between the predicted and actual values. In this case, the algorithm is used for classification tasks to recommend new products to customers. Therefore, RMSE is not relevant to the question asked.
In summary, A, B, and D are the correct options for this scenario.