Q.1. Natural language processing (NLP), a subfield of artificial intelligence an
ID: 3672329 • Letter: Q
Question
Q.1. Natural language processing (NLP), a subfield of artificial intelligence and computational linguistics, is an important component of text mining. What is the definition of NLP? (0.5 Marks)
Q.2. What are the five steps in the backpropagation learning algorithm? (01 Marks)
Q.3. What is k-nearest neighbor (kNN) data mining algorithm? (01 Marks)
Q.4. What are the three steps in the process-based approach to the use of support vector machines (SVMs)? (0.5 Marks)
Q.5. List and briefly describe the six steps of the CRISP-DM data mining process.
Explanation / Answer
Q.1) definition of NLP:
Neuro-Linguistic Programming: "a model of interpersonal communication chiefly concerned with the relationship between successful patterns of behaviour and the subjective experiences (esp. patterns of thought) underlying them" and "a system of alternative therapy based on this which seeks to educate people in self-awareness and effective communication, and to change their patterns of mental and emotional behaviour."
Q.2)
Backpropagation Learning Algorithm
The backpropagation algorithm trains a given feed-forward multilayer neural network for a given set of input patterns with known classifications. When each entry of the sample set is presented to the network, the network examines its output response to the sample input pattern. The output response is then compared to the known and desired output and the error value is calculated. Based on the error, the connection weights are adjusted. The backpropagation algorithm is based on Widrow-Hoff delta learning rule in which the weight adjustment is done through mean square error of the output response to the sample input.
Q.3)
In pattern recognition, the k-Nearest Neighbors algorithm (or k-NN for short) is a non-parametric method used for classification and regression.
In both cases, the input consists of the k closest training examples in the feature space. The output depends on whether k-NN is used for classification or regression:
In k-NN classification, the output is a class membership. An object is classified by a majority vote of its neighbors, with the object being assigned to the class most common among its k nearest neighbors (k is a positive integer, typically small).
If k = 1, then the object is simply assigned to the class of that single nearest neighbor.
In k-NN regression, the output is the property value for the object. This value is the average of the values of its k nearest neighbors.
Q.4)
Support vector machines (SVMs) are a set of supervised learning methods used for classification, regression and outliers detection.
The support vector machines in scikit-learn support both dense (numpy.ndarray and convertible to that by numpy.asarray) and sparse (any scipy.sparse) sample vectors as input. However, to use an SVM to make predictions for sparse data, it must have been fit on such data. For optimal performance, use C-ordered numpy.ndarray (dense) or scipy.sparse.csr_matrix (sparse) with dtype=float64.
Q.5) CRISP-DM methodology
CRISP-DM stands for cross-industry process for data mining. The CRISP-DM methodology provides a structured approach to planning a data mining project. It is a robust and well-proven methodology. We do not claim any ownership over it. We did not invent it. We are however evangelists of its powerful practicality, its flexibility and its usefulness when using analytics to solve thorny business issues. It is the golden thread than runs through almost every client engagement. The CRISP-DM model is shown below.
Phases
Business understanding
The first stage of the CRISP-DM process is to understand what the customer wants to accomplish from a business perspective. Customers often have competing objectives and constraints that must be properly balanced. The analyst’s goal is to uncover important factors that could influence the outcome of the project. Neglecting this step can mean that a great deal of effort is put into producing the right answers to the wrong questions.
Data understanding
The second stage of the CRISP-DM process requires you to acquire the data (or access to the data) listed in the project resources. This initial collection includes data loading, if necessary for data understanding. For example, if you use a specific tool for data understanding, it makes perfect sense to load your data into this tool. If you acquire multiple data sources integrating these together needs to be considered, either here or in the later data preparation phase
Data preparation
Decide on the data to be used for analysis. Criteria include relevance to the data mining goals, quality, and technical constraints such as limits on data volume or data types. Note that data selection covers selection of attributes (columns) as well as selection of records (rows) in a table.
Modeling
As the first step in modeling, select the actual modeling technique that is to be used. Although you may have already selected a tool during the Business Understanding phase, this task refers to the specific modeling technique e.g. decision-tree building with C5.0, or neural network generation with back propagation. If multiple techniques are applied, perform this task separately for each technique.
Evaluation
Previous evaluation steps dealt with factors such as the accuracy and generality of the model. This step assesses the degree to which the model meets the business objectives and seeks to determine if there is some business reason why this model is deficient. Another option is to test the model(s) on test applications in the real application, if time and budget constraints permit. Moreover, evaluation also assesses other data mining results generated. Data mining results involve models that are necessarily related to the original business objectives and all other findings that are not necessarily related to the original business objectives, but might also unveil additional challenges, information, or hints for future directions.
Deployment
This task takes the evaluation results and determines a strategy for deployment. If a general procedure has been identified to create the relevant model(s), this procedure is documented here for later deployment. Our practical experience tells us that it makes sense to consider the ways and means of deployment during the business understanding phase as deployment is crucial to the success of the project. This is where predictive analytics really helps to improve the operation side of your business.
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