Answer:
1. True: Naive Bayes is a linear classifier.
2. False: SVMs are only usable when the classes are linearly separable in the feature space.
3. False: Adding training data always results in a monotonic increase in the accuracy of a Naive Bayes classifier.
4. True: When sufficient data is available, SVMs generally perform as well or better than other common classifiers such as KNN.
5. False: With enough training data, the error of a nearest neighbor classifier always goes down to zero.
Explanation:
Naive Bayes is a linear classifier that leads to a linear decision boundary. It can be applied to a linearly separable problems and when the elements are independent i.e the occurrence of an element doesn't affect the occurrence of another. It can be used for making multi class predictions in artificial intelligence.
The Support Vector Machine (SVM) on the other hand, can either be a non-linear classifier (with RBF kernel) or a linear classifier (with linear kernel). It maximizes the margin of a decision boundary in its mode of operation.
Hence, the SVMs can be used for regression or classification problems.
For example, determining whether an e-mail is a spam or not.
Answer:
Functions.
Explanation:
Move turtle to an absolute position. Move turtle to an absolute position. If the pen is down, a line will be drawn. The turtle's orientation does not change.
Answer:Presentation layer
Explanation: Presentation layer is the sixth layer belonging to the OSI model . It has the functionality of presenting the data or information in a standard and formatted way.The syntax is usually error free , well defined and adequate and thus also called as syntax layer. Presentation layer works between the application layer and the network layer.
It also performs the services of compression, encryption, decryption etc.
Answer: D. Silver (happy to help)
Explanation:
Answer:
This is a Phishing email
Explanation:
Pierre has received a phishing email from a certain scammer. These kinds of emails are an attempt from the scammer to fool the receivers like in this case Pierre and get the personal and financial information that they can use to steal the money from their account. It's hence, a fraudulent attempt to steal the money from the account of a certain person like here it is Pierre.
