Which of the following supervised learning methods CANNOT use categorical input variables (i.e. categorical features, predictors)? O multinomial logistic regression naive Bayes classifier neural net random forest all the listed methods can use categorical features Olinear regression

Explain why one machine learning model is better than another. There are two primary types of categorization: This is shown by the methods of k-nearest neighbour (a), retrospective analysis (c), and expanded knowledge (d).

Could you please explain your choice of machine learning model using some examples?

Explain your machine learning model choice using examples.

Which machine learning task can be used to directly provide a diagnosis for a disease condition, where the input data is a set of blood test results: Clustering Regression Least squares Regularisation Classification Density Estimation Principal component analysis

In a branch of machine learning known as as is, a model is used to produce a forecast utilising characteristics as inputs and delivering a prediction. Some recent cutting-edge models that have found success include the following:

In this section, you will find a brief overview of the process by which the regression models and Artificial Neural Network (ANN) models are created.

What are some methods to determine the best features to use in a machine-learning model?

For unsupervised learning, suggest one way to determine the number of principal components of a dataset which we should use in the system.

Use examples to justify your machine learning model choice. Two novel ways to categorise things include the K-nearest neighbour, looking backward, and gathering supplementary information.