Module 3 (Part 2)

You are part of a data science team that is working for a national fast-food chain. You create a simple report that shows trend: Customers who visit the store more often and buy smaller meals spend more than customers who visit less frequently and buy larger meals. What is the most likely diagram that your team created?

• multiclass classification diagram

• linear regression and scatter plots

• Barplot

• K-means cluster diagram

• classification report

Your company wants to predict whether existing automotive insurance customers are more likely to buy homeowners insurance. It created a model to better predict the best customers contact about homeowners insurance, and the model had a low variance but high bias. What does that say about the data model?

• It was consistently wrong.

• It was inconsistently wrong.

• It was consistently right.

• It was equally right end wrong.

You want to identify global weather patterns that may have been affected by climate change. To do so, you want to use machine learning algorithms to find patterns that would otherwise be imperceptible to a human meteorologist. What is the place to start?

• Find labeled data of sunny days so that the machine will learn to identify bad weather.

• Use unsupervised learning have the machine look for anomalies in a massive weather database.

• Create a training set of unusual patterns and ask the machine learning algorithms to classify them.

• Create a training set of normal weather and have the machine look for similar patterns.

Asian user complains that your company's facial recognition model does not properly identify their facial expressions. What should you do?

• Include Asian faces in your test data and retrain your model.

• Retrain your model with updated hyperparameter values.

• Retrain your model with smaller batch sizes.

• Include Asian faces in your training data and retrain your model.

"you can still make recommendation even if you don’t have specific information about each user" is one of advantage of ...

• Model-Based Recommendation

• Item-Based Recomendation

• Content-Based Recommendation

• User-Based Recommendation

Merekomendasikan item M yang belum digunakan oleh pengguna U tetapi dimiliki telah digunakan oleh pengguna lain yang memiliki preferensi yang mirip dengan pengguna U merupakan cara kerja dari metode rekomendasi ...

• Model-Based

• Item-Based

• User-Based

• Content-Based

Hierarchical Clustering dan Boosting-Based model merupakan interpretable model

• Benar

• Salah

“This transaction overall dropped since the new customer transaction is dropped too, we need to find out how to increase new customer now” is an example of a statement from

• Correlation

• Causality

• Predictive Power

"Which customer is at risk to leave our service"

This statement includes a prediction statement

• True

• False

Companies that already have a "Data-Aware Mindset" have a higher Business Value than companies that have a "Data-Savvy Mindset".

• True

• False