Answer:
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Answer:
Collaborative filtering
Explanation:
This is one out of five on the Recommender system apart from most popular items, Association and Market Basket based Analysis, Content-based analysis, self and hybrid analysis where we use both content-based and collaborative based approach together. And the Recommender system is a very important topic in Data science. For this question, remember that Collaborative filtering focuses on user and various other user's choices which are mathematically alike to concerned users, and which we find with the study of a large data set. Thus, we can predict from our above study that what are going to be likes of concerned users, and at the item level, whether that item will be liked by the concerned user or not. And this is prediction, and we use this approach in Machine learning these days. For this question, and as mentioned in question the requirements, answer is Collaborative filtering.
Answer:
The answer is "Option b"
Explanation:
This method is also known as the model is continuously developed, evaluated, applied and enhanced. It enables firms to manage transactions with its all-out returns, they can be used to calculate the final 7 digits of the Online charge level, and the wrong option can be described as follows:
- In option a, It first find then arranging all the data.
- In option c, It provides essential updates and saves into the file.
- In option d, It is an ideal plan for a lifetime operation.
Answer:You can only find REaccuracy if you know the actual “true” measurement… something that's difficult to do unless you're measuring against the atomic clock. The formula is: REaccuracy = (Absolute error / “True” value) * 100%.
Explanation:
<span> You inquire about a credit card charge has NO impact on your credit score~</span>
