Learn how holdout data is used for testing and validating machine learning models in this expert explanation for the IBM Artificial Intelligence Fundamentals certification exam.
Table of Contents
Question
In the simulation, you used training data and holdout data when setting up the details of the experiment. Ninety percent of the data was used to train the algorithms.
Which of the following describes how the holdout data was used?
A. Backup data
B. Testing data
C. Training data
D. Reserve data
Answer
B. Testing data
Explanation
The Training data split slider allows you to choose how much of the data set to use for training and how much to use for testing. In the simulation, you used 90% of the data set to train the algorithms using supervised learning, since Risk or No Risk was specified. The remaining 10% of the data set was used as a test to determine how well the algorithms performed.
The holdout data was used as testing data
In machine learning, holdout data refers to a portion of the dataset that is not used during the training phase. Instead, it is reserved to test the model after training, to evaluate its performance and generalization to new, unseen data. This helps in assessing how well the model is likely to perform in real-world scenarios.
The purpose of holdout data is to serve as an independent testing set to evaluate the performance and generalization ability of the trained models. After the algorithms are trained on the training data, they are applied to make predictions on the unseen holdout data. By comparing the predictions against the actual known outcomes in the holdout set, we can assess metrics like accuracy, precision, recall, etc. to measure how well the models perform on new data.
Using a separate holdout set for testing is crucial because it provides an unbiased estimate of the models’ real-world performance. If we were to test on the same data used for training, the evaluation would be overly optimistic since the models may have overfit and memorized the training examples. The holdout set acts as a proxy for fresh data the models would encounter in live deployment.
So in summary, the holdout data in this scenario is used as a testing set to validate and assess the trained models before putting them into production. It is not used as backup, training, or reserve data.
The use of holdout data for testing is a key best practice in machine learning to ensure models generalize well and achieve strong performance on unseen data. Let me know if you need any clarification or have additional questions!
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