Answer:
There is a wide application of kinematics; it is also used in astrophysics. In mechanical engineering, robotics, and biomechanics, it is used to describe the motion of the system of the joint parts.
The biggest security issue with using social networking sites to market your listings is Criminals may use social networking sites to identify your personal data.
<h3>What is a social marketing site?</h3>
A social marketing site is a site where people from around the world are connected in one place and share their thoughts and photos and other data.
The social sites are on the internet and there is a chance of stealing data and it identity theft, phishing, online predators, internet fraud, and other cybercriminal attacks are also some risks.
Thus, the largest security concern with using social networking sites to sell your listings is that thieves could use these sites to find personal information about you.
To learn more about social marketing sites, refer to the link:
brainly.com/question/15051868
#SPJ4
Answer:
The drill down term is basically used in the information technology for the explore the multidimensional information or data by navigating the different layers of the data from the web page applications.
Drill down basically involve in the database by accessing the specific information through the database queries. Each query basically increase the data granularity. This term is also involve with the link for represent the details more specifically.
The drill down is the simple approach or technique for dividing the complex problems into small parts so that it make the technique more efficient.
Answer:
a. This is an instance of overfitting.
Explanation:
In data modeling and machine learning practice, data modeling begins with model training whereby the training data is used to train and fit a prediction model. When a trained model performs well on training data and has low accuracy on the test data, then we say say the model is overfitting. This means that the model is memorizing rather Than learning and hence, model fits the data too well, hence, making the model unable to perform well on the test or validation set. A model which underfits will fail to perform well on both the training and validation set.
