FIT5201 - Machine Learning - Engineering Assignment Help

Assignment Task

Objectives
This assignment assesses your understanding of model complexity, model selection, uncertainty in prediction with bootstrapping, and probabilistic machine learning, and linear models for regression and classification, covered in Modules 1, 2, and 3. The total marks of this assignment is 150. This assignment constitutes 25% of your final mark for this unit.

Section A. Model Complexity and Model Selection
In this section, you study the effect of model complexity on the training and testing error. You also demonstrate your programming skills by developing a regression
algorithm and a cross-validation technique that will be used to select the models with the most effective complexity.

Background.

A KNN regressor is similar to a KNN classifier (covered in Activity 1.1) in that it finds the K nearest neighbors and estimates the value of the given test point based on the values of its neighbours. The main difference between KNN regression and KNN classification is that KNN classifier returns the label that has the majority vote in the neighborhood, whilst KNN regressor returns the average of the neighbors’ values. In Activity 1 of Module 1, we use the number of misclassifications as the measurement of training and testing errors in KNN classifier.
For KNN regressor, you need to choose another error function as the measurement of training errors and testing errors.

Question 1 [KNN Regressor]
I. Implement the KNN regressor function:
knn(train.data, train.label, test.data, K=3) which takes the training data and their labels (continuous values), the test set, and the size of the neighborhood (K). It should return the regressed values for the test data points. Note that, you need to use a distance function to choose the neighbors. The distance function used to measure
the distance between a pair of data points is Manhattan distance function.

II. Plot the training and the testing errors versus 1/K for K=1,.., 35 in one plot, using the Task1A_train.csv and Task1A_test.csv datasets provided for this assignment. Save the plot in your Jupyter Notebook file for Question 1. Report your chosen error function in your Jupyter Notebook file. 

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