Tutorial _W10_slides (4)

What are the advantages of neural network modeling over standard regression model.What factors motivate non-parametric regression

How does data preprocessing contribute to better machine learning model performance?

Discuss what the Naïve Bayes algorithm is and when it may be used for data analysis

In what ways does a descriptive model differ from a prescriptive one?

How do you assess logistic regression versus simple linear regression models in machine learning concept ?

Computer Science Create a machine learning model based on regression or classification method. Select a real dataset and based on the dataset develop the machine learning model. You have to describe all the steps of the project in details

What abilities are required for predictive modeling?

When you consider email, what pictures come to mind? How is an email sent from point A to point B? List all you have learnt to date. Who knows the reason for these variances, and if so, what are they? Do your models have varying degrees of complexity?

What is the point of TOM's benchmarking?

Define the term "Bayesian classification." What are the benefits of it?

What skills are needed for predictive modelling?

What skills are required for working with predictive models?

What is predictive analysis?

How can data-flow diagrams be used as analysis tools?

Please match the evaluation methods with the supervised learning tasks. Answer is Classification or Regression ? Sensitivity Root mean squared error Classification accuracy F1-score Mean absolute error R-squared

Explain the concept of mutation-driven testing for machine learning models and its applications in ensuring model robustness and reliability.

Define sum of squares regression (SSR)?

How do stochastic models and probabilistic approaches impact system modeling and analysis?

explain the difference between traditional approach and predictive approach

Discuss innovative uses of binary variables in model formulation?