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

Use an example to demonstrate the difference between supervised and unsupervised learning.

We are collecting data for estimating the transition function and the reward function. What is the type of this reinforcement learning algorithm?   Select one answer Model-based learning Model-free learning Q-Learning Temporal Difference

Justify your preferred machine learning model's use in a given scenario. There are two primary ways to classify items: (A) reminiscing, (C) using K-nearest neighbour, and (D) gaining insight.

In what ways may machine learning models be trained using log data?

List different methods for sequential supervised learning.    Only provide methods there is no need for any descriptions. And don't give handwritten solutions

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

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.