True or false? If false, explain: a) a cateforical variable having 6 levels would require 5 indicator variables for use in a regression model b) when specifying a categorical variable in minitab, the default reference category will always be the first ctegory when listed alpha-numerically. The reference category may be changed if desired. c) a model contains one quantitative and one binary predictor varible. If full interaction model is fit, the resulting model would be displayed as two lines with potentialsly different slopes.

Q: Why is Adjusted R preferred to R to assess the fit of a regression model? Because R² substantially…

A: Answer: Because R2  always increases when variables are added to the model

Q: In classification and regression trees (CART), it is done by the model itself, based on how dirty it…

A: The Answer is

Q: We need to know the criteria for determining if a model is prescriptive.

A: The correct answer for the above mentioned question is given in the following steps for your…

Q: Consider the model selection procedure where we choose the degree of polynomial, d, using a cross…

A: A model selection procedure is used to select the most appropriate model for the data set, and the…

Q: What are the major differences in use-cases for linear regression as compared with non-linear types?

A: In business, linear regressions can be used to evaluate trends and make estimates or forecasts…

Q: How can you tell if a model meets the requirements for proportionality and additivity?

A: Introduction   An presumption is that a simplifying condition is taken into account in the research…

Q: If a model is prescriptive, what makes it so?

A: Presentation: A descriptive model is used to illustrate how a system or other item interacts with…

Q: For the Iteration and Incrementation life-cycle model do the following:

A: The Iterative and Incremental life-cycle model is a developmental approach that combines iterative…

Q: What is the main purpose of regularization when training predictive models   What is the role of a…

A: Regularization significantly reduces the variance of the model without substantial increase in its…

Q: With neat diagram, explain the iterative process of calibrating a model.

A: Given question has asked to explain the iterative process of calibrating a model with neat diagram.

Q: How do you choose the best Linear Regression training strategy to utilise when you have a large…

A: Linear Regression training strategy is a machine learning algorithm based on the supervised…

Q: Does a higher value of k lead to a more complex model, or a less complex model?

A: Given question is under K-NN classification model. Complexity of model depends on the value of k.

Q: In classification and regression trees (CART), it is performed by the model itself based on impurity…

A: Yes, selecting features prior to conducting a cart analysis is critical. Even if we choose all of…

Q: f we add more independent variables into the model:   A. The adjusted R2 value will increase.   B.…

A: Answer :

Q: How do you analyse the performance of the predictions generated by regression models versus…

A: Supervised machine learning and Unsupervised machine learning are the two main categories of machine…

Q: Match each the following example datasets (X,y) on the left to the most logical type of supervised…

A: Multivariate Linear Regression: Multiple independent variables contributing to the dependent…

Q: If we evaluate a classification model(such as SVC) using the K-fold cross-validation and we set K =…

A: 9 times classification model will be trained and evaluated.

Q: Consider the following data Classification Model where YACT is your actual observation and YPRED is…

A:

Q: The hyper-parameters of a model must NOT be tuned on the test data ( i.e, the data used to evaluate…

A: hyper-parameters A hyperparameter is machine learning is a parameter whose quantity is utilised to…

Q: The following describes sensitivity, EXCEPT the ability to include new data sets and/or remove old…

A: Data sensitivity is related to information that should be protected from unauthorized access or…

Q: What is the best way to decide how many epochs of training to perform? It is always obvious looking…

A: Epoch meaning:- An epoch is a term used in machine learning and indicates the number of passes of…

Q: Use a simple example to explain the iterati

A: Dear Student, The answer to your question is given below -

Q: Gradient decent will always find global minimum regardless of the initial values of model parameters…

A: Gradient Descent is an algorithm that is supposed to identify the best or optimal points, although…

Q: GLLS

A: The GLLS (Generalized Least Squares) method is a regression technique that can be used to fit a…

Q: The metrics that are calculated for the training set measures the goodness of fit of the fitted…

A: Training data is the initial data used to train machine learning models.

Q: Which of the following statements about confusion matrix is wrong A) Confusion matrix is a…

A: Confusion Matrix: A confusion matrix is an NxN matrix representation format where N indicates that…

Q: The data normalization process is one of the pre-processing stages needed to improve model…

A: Normalization is used when the the datasets have dat in different ranges. So normalization changes…

Q: How can you recognize whether a model satisfies proportionality andadditivity?

A: An presumption is that a simplifying condition is taken into account in the research method such…

Q: Write the objective function that can be used to determine the regression model parameters. How is…

A: The solution to the given question is: The objective function is the sum of squared errors (SSE).…

Q: Draw logistic regression flow chart First design the question (choose x1 and x2 values for each…

A: solution:   Logistic regression pseudocode. from sklearn preprocessing import standard scalar…

Q: Question 1: Rule and Pattern Assessment Measures a) Show that if X and Y are independent, then…

A: The conviction is used in rule assessment for measuring the expected error of the rule.

Q: While it is preferable to utilize either descriptive or prescriptive models, which one is more…

A: Introduction : Prescriptive and descriptive models have different effectiveness depending on their…

Q: A learning curve is defined as: Ⓒa. A plot of the performance of the classifier on the y axis versus…

A: Learning curve is present in the machine learning. It also known as graphical record. In the…

Q: a. Feature selection can be done through both filter and wrapper method. Which of these methods is…

A: Given: Explain feature selection methods.

Q: What is the primary objective of linear regression in machine learning? A) Maximizing accuracy of…

A: A key method in machine learning is called linear regression, which uses a linear equation to…

Q: Only by seeing how well a model performs on test data can its accuracy be determined. elaborate on;…

A: Introduction: The typical procedure for assessing machine learning models is to fit and evaluate…

System Model Approaches

The systems model approach is a process-oriented representation that emphasizes the impact or flow of information between modules. The multidisciplinary study of the use of models to design and create systems in business and IT development is known as systems model approach. It models how processes interact and what these processes do, but it does not go into detail about how these processes are implemented. Functional decomposition may be utilized to enhance the models employed in this technique, and they can be coupled to requirements models for additional system partitioning.