QUESTION: 81

A Data Scientist is training a multilayer perception (MLP) on a dataset with multiple classes. The target class of interest is unique compared to the other classes within the dataset, but it does not achieve and acceptable recall metric. The Data Scientist has already tried varying the number and size of the MLP’s hidden layers, which has not significantly improved the results. A solution to improve recall must be implemented as quickly as possible.

Which techniques should be used to meet these requirements?

Gather more data using Amazon Mechanical Turk and then retrain
Train an anomaly detection model instead of an MLP
Train an XGBoost model instead of an MLP
Add class weights to the MLP’s loss function and then retrain

Answer(s): D

Reference:

https://androidkt.com/set-class-weight-for-imbalance-dataset-in-keras/

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QUESTION: 82

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?

Streaming classification
Binary classification
Multi-category classification
Regression classification

Answer(s): B

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QUESTION: 83

A real estate company wants to create a machine learning model for predicting housing prices based on a historical dataset. The dataset contains 32 features.

Which model will meet the business requirement?

Logistic regression
Linear regression
K-means
Principal component analysis (PCA)

Answer(s): B

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QUESTION: 84

A Machine Learning Specialist is applying a linear least squares regression model to a dataset with 1,000 records and 50 features. Prior to training, the ML Specialist notices that two features are perfectly linearly dependent.

Why could this be an issue for the linear least squares regression model?

It could cause the backpropagation algorithm to fail during training
It could create a singular matrix during optimization, which fails to define a unique solution
It could modify the loss function during optimization, causing it to fail during training
It could introduce non-linear dependencies within the data, which could invalidate the linear assumptions of the model

Answer(s): B

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Free MLS-C01 Exam Braindumps (page: 21)

QUESTION: 81

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QUESTION: 82

QUESTION: 83

QUESTION: 84