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NEW QUESTION 35
A Machine Learning Specialist works for a credit card processing company and needs to predict which transactions may be fraudulent in near-real time. Specifically, the Specialist must train a model that returns the probability that a given transaction may fraudulent.
How should the Specialist frame this business problem?

A. Binary classificationB. Regression classificationC. Streaming classificationD. Multi-category classification

Answer: D

 

NEW QUESTION 36
A data engineer at a bank is evaluating a new tabular dataset that includes customer dat a. The data engineer will use the customer data to create a new model to predict customer behavior. After creating a correlation matrix for the variables, the data engineer notices that many of the 100 features are highly correlated with each other.
Which steps should the data engineer take to address this issue? (Choose two.)

A. Apply min-max feature scaling to the dataset.B. Use a linear-based algorithm to train the model.C. Apply one-hot encoding category-based variables.D. Remove a portion of highly correlated features from the dataset.E. Apply principal component analysis (PCA).

Answer: A,E

Explanation:
Reference:
https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html

 

NEW QUESTION 37
A Machine Learning Specialist deployed a model that provides product recommendations on a company's website Initially, the model was performing very well and resulted in customers buying more products on average However within the past few months the Specialist has noticed that the effect of product recommendations has diminished and customers are starting to return to their original habits of spending less The Specialist is unsure of what happened, as the model has not changed from its initial deployment over a year ago Which method should the Specialist try to improve model performance?

A. The model needs to be completely re-engineered because it is unable to handle product inventory changesB. The model's hyperparameters should be periodically updated to prevent driftC. The model should be periodically retrained using the original training data plus new data as product inventory changesD. The model should be periodically retrained from scratch using the original data while adding a regularization term to handle product inventory changes

Answer: C

 

NEW QUESTION 38
A Data Scientist uses logistic regression to build a fraud detection model. While the model accuracy is 99%, 90% of the fraud cases are not detected by the model.
What action will definitively help the model detect more than 10% of fraud cases?

A. Using regularization to reduce overfittingB. Using oversampling to balance the datasetC. Using undersampling to balance the datasetD. Decreasing the class probability threshold

Answer: D

Explanation:
Decreasing the class probability threshold makes the model more sensitive and, therefore, marks more cases as the positive class, which is fraud in this case. This will increase the likelihood of fraud detection. However, it comes at the price of lowering precision.

 

NEW QUESTION 39
A Data Scientist is developing a machine learning model to predict future patient outcomes based on information collected about each patient and their treatment plans. The model should output a continuous value as its prediction. The data available includes labeled outcomes for a set of 4,000 patients. The study was conducted on a group of individuals over the age of 65 who have a particular disease that is known to worsen with age.
Initial models have performed poorly. While reviewing the underlying data, the Data Scientist notices that, out of 4,000 patient observations, there are 450 where the patient age has been input as 0. The other features for these observations appear normal compared to the rest of the sample population.
How should the Data Scientist correct this issue?

A. Use k-means clustering to handle missing features.B. Replace the age field value for records with a value of 0 with the mean or median value from the dataset.C. Drop the age feature from the dataset and train the model using the rest of the features.D. Drop all records from the dataset where age has been set to 0.

Answer: B

 

NEW QUESTION 40
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