3.3.3 Module 3 Quiz – Big Data, AI and ML Exam Answers 100% 2023 2024
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When several items are grouped, which type of machine learning algorithm can determine which items in the group predict the presence of other items?
- Association
- Classification
- Clustering
- Regression
Answers Explanation & Hints:
The machine learning algorithm that can determine which items in a group predict the presence of other items is called Association. Association rule mining is a type of unsupervised learning technique used to discover interesting relationships, associations or correlations between variables in large datasets. It involves identifying frequently occurring patterns or itemsets in a dataset and using these patterns to predict the likelihood of the occurrence of other items. In the context of grouping items, association rule mining can be used to identify which items tend to appear together, and to predict the presence of certain items based on the presence of others.
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Which machine learning algorithm uses data sets verified by experts as its learning basis?
- Association
- Clustering
- Routing
- Supervised
Answers Explanation & Hints:
The machine learning algorithm that uses data sets verified by experts as its learning basis is called Supervised Learning. In supervised learning, the algorithm is trained on a labeled dataset, which means that the input data is already tagged with the correct output. This labeled dataset is created and verified by experts who have the necessary domain knowledge to identify and label the data correctly. The algorithm then uses this labeled data to learn the underlying patterns and relationships between the input and output variables, which it can then apply to new, unseen data to make predictions or classifications. Supervised learning is commonly used in applications such as image recognition, speech recognition, and predictive analytics.
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Which method describes how a machine learns using the reinforcement machine learning model?
- Autonomously discovering patterns in data.
- Human interactions to label data read accuracy.
- Discovering groups of items frequently observed together.
- Trial and error using feedback from the action and experiences.
Answers Explanation & Hints:
The method that describes how a machine learns using the reinforcement machine learning model is trial and error using feedback from the action and experiences. Reinforcement learning is a type of machine learning algorithm that involves an agent learning through interaction with its environment. The agent learns by performing actions and receiving feedback or rewards from the environment. The goal of the agent is to learn a policy that maximizes the total reward it receives over time. The agent learns through trial and error, adjusting its policy based on the rewards it receives from its actions. This approach is often used in applications such as robotics, gaming, and autonomous systems.
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Which step in the machine learning process transforms data into a structured format by removing missing data and corrupted observations?
- Testing data
- Learning data
- Preparing data
- Model evaluation
Answers Explanation & Hints:
The step in the machine learning process that transforms data into a structured format by removing missing data and corrupted observations is called Data Preparation. Data preparation, also known as data cleaning or data preprocessing, is a critical step in the machine learning process that involves transforming raw data into a format that can be used by the learning algorithm. This includes identifying and handling missing or corrupted data, handling outliers and anomalies, transforming data types, and normalizing or scaling data to a common range. Data preparation is essential to ensure the quality and accuracy of the machine learning model, as the quality of the input data can significantly impact the accuracy of the model’s predictions.
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In training a pattern recognition system which data set measures the accuracy achieved by the model?
- Model data set
- Testing data set
- Sample data set
- Training data set
Answers Explanation & Hints:
In training a pattern recognition system, the data set that measures the accuracy achieved by the model is the testing data set. The testing data set is a subset of the original data that is not used in the training process but is instead used to evaluate the performance of the trained model. The testing data set is labeled with known outputs, and the model’s predictions are compared against these known outputs to calculate metrics such as accuracy, precision, and recall. By evaluating the model’s performance on a separate testing data set, the model’s ability to generalize to new and unseen data can be assessed, and any overfitting or underfitting issues can be identified and addressed.