Best Performing Model Essay
Q1: Describe how you trained and evaluated your models. Discuss how you were deciding what to try next.
The task in this assignment was to correctly classify each instance’s genre of music. Taking this in mind I decided the best way to evaluate my models was to use the correctly classified instances percentage, as the way of judging which model was better than the others. I started by training all the models we had covered during the lectures then used the Multilayer Perception model that I had researched. I began training these models using their pre-set parameters. I used the correctly classified instance percentage to help me decide on the models that I would explore further. I chose the models that had scored over 90% when predicting the training data. This left …show more content…
In your own words explain the algorithm/intuition behind it. What was your worst performing model? Why do you think it did not perform well?
Best performing model
My best performing model was the using the multilayer perception classifier with a learning rate of 0.2 . I filtered down the attributes to the most useful 120. I chose 120 because it when I ran the ‘filterattributeeval’ search with the ‘ranker’ function I took all the attributes ranked 0.1 or higher. I felt that anything ranked below 0.1 could be an interference with the accuracy of the prediction. Filtering my attributes to a lower amount also helped to speed up the run time of the classifier as it didn’t have to compute as many parameters.
How the model works (Beri, 2013)
The multilayer perception algorithm is a network made up of many neurons which is split into many layers:
The input layer – the number of neurons depends on the number of inputs/instances we are trying to predict.
The hidden layers – can be one or more layers between the input and output layers. Its job is to convert the input into the output or in the case of my assignment, place the input into the correct genre of
The task in this assignment was to correctly classify each instance’s genre of music. Taking this in mind I decided the best way to evaluate my models was to use the correctly classified instances percentage, as the way of judging which model was better than the others. I started by training all the models we had covered during the lectures then used the Multilayer Perception model that I had researched. I began training these models using their pre-set parameters. I used the correctly classified instance percentage to help me decide on the models that I would explore further. I chose the models that had scored over 90% when predicting the training data. This left …show more content…
In your own words explain the algorithm/intuition behind it. What was your worst performing model? Why do you think it did not perform well?
Best performing model
My best performing model was the using the multilayer perception classifier with a learning rate of 0.2 . I filtered down the attributes to the most useful 120. I chose 120 because it when I ran the ‘filterattributeeval’ search with the ‘ranker’ function I took all the attributes ranked 0.1 or higher. I felt that anything ranked below 0.1 could be an interference with the accuracy of the prediction. Filtering my attributes to a lower amount also helped to speed up the run time of the classifier as it didn’t have to compute as many parameters.
How the model works (Beri, 2013)
The multilayer perception algorithm is a network made up of many neurons which is split into many layers:
The input layer – the number of neurons depends on the number of inputs/instances we are trying to predict.
The hidden layers – can be one or more layers between the input and output layers. Its job is to convert the input into the output or in the case of my assignment, place the input into the correct genre of