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Answer:
Kindly check Explanation.
Explanation:
Machine Learning refers to a concept of teaching or empowering systems with the ability to learn without explicit programming.
Supervised machine learning refers to a Machine learning concept whereby the system is provided with both features and label or target data to learn from. The target or label refers to the actual prediction which is provided alongside the learning features. This means that the output, target or label of the features used in training is provided to the system. this is where the word supervised comes in, the target or label provided during training or teaching the system ensures that the system can evaluate the correctness of what is she's being taught. The actual prediction provided ensures that the predictions made by the system can be monitored and accuracy evaluated.
Hence the main difference between supervised and unsupervised machine learning is the fact that one is provided with label or target data( supervised learning) and unsupervised learning isn't provided with target data, hence, it finds pattern in the data on it's own.
A to B mapping or input to output refers to the feature to target mapping.
Where A or input represents the feature parameters and B or output means the target or label parameter.
Explanation:
Both Primary Key and Candidate Key are the attributes that are used to access tuples from a table. These (Primary key and Candidate key) are also can be used to create a relationship between two tables.
- A Candidate Key can be any column or a combination of columns that can qualify as a unique key in the database. Each Candidate Key can qualify as a Primary Key.
- A Primary Key is a column or a combination of columns that uniquely identify a record. The primary key is a minimal super key, so there is one and only one primary key in any relationship.
<em> The main difference between them is that a primary key is unique but there can be many candidate keys. </em>
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