By Anjeev Kr Singh – Computer Science Educator
Published on : September 19, 2022 | Updated on : February 9, 2023
9. Draw the graphical representation of Regression AI model. Explain in brief.
Answer: Regression: These models work on continuous data to predict the output based on patterns. For example, if you wish to predict your next salary, then you would put in the data of your previous salary, any increments, etc., and would train the model. Here, the data which has been fed to the machine is continuous.
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Regression is the process of finding a model for distinguishing the data into continuous real values instead of using discrete values. It can also identify the distribution movement depending on the historical data.
10. Draw the graphical representation of Clustering AI model. Explain in brief.
Answer: Clustering: It refers to the unsupervised learning algorithm which can cluster the unknown data according to the patterns or trends identified out of it. The patterns observed might be the ones which are known to the developer or it might even come up with some unique patterns out of it.
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Clustering is the task of dividing the data points into a number of groups such that data points in the same groups are more similar to other data points in the same group and dissimilar to the data points in other groups. It is basically a collection of objects on the basis of similarity and dissimilarity between them.
11. Explain Data Exploration stage.
Answer: In this stage of project cycle, we try to interpret some useful information out of the data we have acquired. For this purpose, we need to explore the data and try to put it uniformly for a better understanding. This stage deals with validating or verification of the collected data and to analyze that:
• The data is according to the specifications decided.
• The data is free from errors.
• The data is meeting our needs.
12. What are the features of an Artificial Neural Network?
Answer: Any Artificial Neural Network, irrespective of the style and logic of implementation, has a few basic features as given below.
• The Artificial Neural Network systems are modelled on the human brain and nervous system.
• They are able to automatically extract features without feeding the input by programmer.
• Every node of layer in a Neural Network is compulsorily a machine learning algorithm.
• It is very useful to implement when solving problems for very huge datasets.
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• It can work with incomplete knowledge and may produce output even with incomplete information.
• It has fault tolerance which means that corruption of one or more cells of ANN does not prevent it from generating output.
• It has the ability to learn events and make decisions by commenting on similar events.
• It has Parallel processing capability i.e. ANN have numerical strength that can perform more than one job at the same time.
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• Neural Networks have the ability to learn by themselves and produce the output that is not limited to the input provided to them.
• The input is stored in its own networks instead of a database; hence the loss of data does not affect its working.
• These networks can learn from examples and apply them when a similar event arises, making them able to work through real-time events.
• Even if a neuron is not responding or a piece of information is missing, the network can detect the fault and still produce the output.
• They can perform multiple tasks in parallel without affecting the system performance
13. What is the purpose of getting AI Ready?
Answer: The world is changing with each day and we have huge data coming our way. The purpose of getting AI ready means taking steps to collect data around relevant systems, equipment, and procedures; and storing and curating that data in a way that makes it easily accessible to others for use in future AI applications.
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The purpose of getting AI ready specifies the responsible and optimum use of huge amount of data around us to create and implement into such systems and applications which should make life of future generations more organized and sustainable. This process may lead to better lives for mankind.
14. What are the different types of sources of data from where we can collect reliable and authentic datasets? Explain in brief.
Answer: Data can be a piece of information or facts and statistics collected together for reference or analysis. Whenever we want an AI project to be able to predict an output, we need to train it first using data There could be many ways and sources from where we can collect reliable and authentic datasets namely Surveys, Web scrapping, Sensors, Cameras, Observations, Research, Investigation, API etc.
Sometimes Internet is also used to acquire data but the most important point to keep in mind is that the data should be taken from reliable and authentic websites only. Some reliable data sources are UN, Google scholar, Finance, CIA, Data.gov etc.
Four (04) Mark Questions
1. Explain the AI Project Cycle in detail.
Answer: The steps involved in AI project cycle are as given:
• The first step is Scope the Problem by which, you set the goal for your AI project by stating the problem which you wish to solve with it. Under problem scoping, we look at various parameters which affect the problem we wish to solve so that the picture becomes clearer
• Next step is to acquire data which will become the base of your project as it will help you in understanding what the parameters that are related to problem scoping.
• Next, you go for data acquisition by collecting data from various reliable and authentic sources. Since the data you collect would be in large quantities, you can try to give it a visual image of different types of representations like graphs, databases, flow charts, maps, etc. This makes it easier for you to interpret the patterns in which your acquired data follows.
• After exploring the patterns, you can decide upon the type of model you would build to achieve the goal. For this, you can research online and select various models which give a suitable output.
• You can test the selected models and figure out which is the most efficient one.
• The most efficient model is now the base of your AI project and you can develop your algorithm around it.
• Once the modelling is complete, you now need to test your model on some newly fetched data. The results will help you in evaluating your model and hence improving it.
Finally, after evaluation, the project cycle is now complete and what you get is your AI project.
CBSE Question Bank – AI – Class 10 – Chapter 3 AI Project Cycle
2. Explain the relation between data size and model performance of an Artificial Neural Network.
Answer: The basis for any kind of AI development is BIG DATASET. The performance of any AI based application depends on the data supplied
ANN models are also known as Learning models and are used for prediction purposes. These are mostly developed without paying much cognizance to the size of datasets that can produce models of high accuracy and better generalization. Although, the general belief is that, large dataset is needed to construct a predictive learning model. To describe a data set as large in size, perhaps, is circumstance dependent, thus, what constitutes a dataset to be considered as being big or small is somehow vague.
In fact, the quantity of data partitioned for the purpose of training must be of good representation of the entire sets and sufficient enough to span through the input space. It must be authentic and relevant to give better model performance.
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