Learn how machine learning algorithms constantly test and adjust themselves based on new data to improve their performance and accuracy over time. A key concept covered in the IBM Artificial Intelligence Fundamentals certification.
Table of Contents
Question
Which of the following best describes how machine learning’s trial-and-error learning process works?
A. Machine learning relies solely on preset algorithms to generate results without any adjustment or modification.
B. Machine learning learns by memorizing all possible outcomes and selecting the most common one as the correct answer.
C. Machine learning makes random adjustments to its algorithms without testing them against existing data.
D. Machine learning adjusts itself by constantly testing new data against its corpus, making modifications based on established patterns.
Answer
D. Machine learning adjusts itself by constantly testing new data against its corpus, making modifications based on established patterns.
Explanation
Machine learning adjusts itself through trial and error by continuously testing new data or calculation results against its corpus. It modifies established patterns based on this comparison, constantly striving for improvement.
Machine learning algorithms work through a trial-and-error process where they are constantly making adjustments and modifications based on new data in order to improve their performance.
The key steps are:
- The ML model makes a prediction or takes an action.
- The results are compared against the ground truth from the training data.
- Errors or inaccuracies in the output are identified.
- The model’s internal parameters are adjusted slightly to reduce those errors.
- Steps 1-4 are repeated many times as the model ingests more training data.
Over many iterations of this process, the machine learning algorithm gradually becomes more accurate and optimizes itself for the given task. The continual testing of outputs against real-world data and feedback allows the model to identify patterns and correlations in order to improve its predictions and decision making.
This is in contrast to using hard-coded, preset algorithms that don’t change, or approaches like memorizing data or making arbitrary guesses and changes not grounded in data. The core power of machine learning comes from this iterative, data-driven self-adjustment process to produce models highly tuned for a specific application.
Machine learning’s trial-and-error learning process involves adjusting itself by constantly testing new data against its corpus and making modifications based on established patterns. Let’s break down the options:
A. Machine learning relies solely on preset algorithms: This statement is not accurate. Machine learning algorithms learn from data and adapt their parameters based on the training process. They are not fixed by preset algorithms.
B. Machine learning learns by memorizing all possible outcomes: Memorization is not the primary goal of machine learning. Instead, it aims to generalize patterns from data to make predictions or classifications.
C. Machine learning makes random adjustments without testing: Random adjustments without testing would not lead to effective learning. Machine learning relies on systematic optimization techniques (e.g., gradient descent) to improve model performance.
D. Machine learning adjusts itself by constantly testing new data: This statement aligns with the trial-and-error learning process. Machine learning models iteratively learn from data, evaluate their performance, and update their parameters to improve predictions.
Therefore, the correct answer is D. Machine learning adjusts itself by constantly testing new data against its corpus, making modifications based on established patterns.
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