Explore the different bad data scenarios used when interacting with Teachable Machine in the Google AI for Anyone certification exam. Learn how to identify the correct options and ace your exam!
Table of Contents
Question
In the interaction with Teachable Machine using bad data, you were asked to try different scenarios. Identify those from the options provided below.
A. Training the machine by providing a label but training with a different hand pose, for example, the label is Rock while the hand poses are of Scissors.
B. Training the machine by adding a fourth class and labelling it as “erroneous data”.
C. Training the machine with correct values to the labels but under different lighting conditions.
D. Training the machine to detect Rock, Paper, and Scissors hand poses while providing mixed-up data.
Answer
A. Training the machine by providing a label but training with a different hand pose, for example, the label is Rock while the hand poses are of Scissors.
C. Training the machine with correct values to the labels but under different lighting conditions.
D. Training the machine to detect Rock, Paper, and Scissors hand poses while providing mixed-up data.
Explanation
The correct options for the scenarios tested with bad data in the Teachable Machine interaction are:
A. Training the machine by providing a label but training with a different hand pose, for example, the label is Rock while the hand poses are of Scissors.
This scenario tests how the machine learning model reacts when the label and the actual data provided do not match, leading to incorrect classifications.
C. Training the machine with correct values to the labels but under different lighting conditions.
This scenario evaluates the model’s performance when the data is collected under varying lighting conditions, which can impact the model’s ability to accurately classify the hand poses.
D. Training the machine to detect Rock, Paper, and Scissors hand poses while providing mixed-up data.
This scenario assesses the model’s performance when the training data is inconsistent or mixed-up, which can lead to poor classification results.
Option B, “Training the machine by adding a fourth class and labelling it as ‘erroneous data’,” is not one of the scenarios tested in the interaction with Teachable Machine using bad data.
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