Machine Learning Case Study Assessment 1

Assignment Task

Introduction

In this assessment, you will experiment with a classifier using a standard dataset. You will analyse the data and evaluate the results obtained by the classifier. Based on your understanding of the method and data, you will measure performance and propose steps for improvement.

Questions about this assessment can be posted to the Assessment 1 Discussion Board and you should consult the assessment rubric when preparing your submission.

Purpose

This assessment will provide you with the opportunity to:

• frame classification methods, introduced in previous courses, in the context of machine learning
• demonstrate the importance of good practice for data handling and cleaning
• practise use of Python tool suite for machine learning tasks
• demonstrate informed analysis of performance and

Outcomes

This assessment maps to the following course learning outcomes:

• utilise industry standard software tools to devise models and implement machine learning tasks on real datasets
• train deep neural networks to implement machine learning tasks
• design data management procedures to enable accurate application of machine learning
• critically review the significance and validity of results and solutions, and identify sources of error.

Your tasks

This assessment is divided into three tasks:

1. Prepare and analyse the dataset
2. Train a regularised stochastic gradient descent classifier
3.  Evaluate the

Directions

In this assessment, you will train a classifier to detect breast cancer from a set of characteristics of the cell nuclei in an image of a fine needle aspirate of a breast mass. The Breast Cancer Wisconsin dataset is available from the collection of example datasets in scikit learn. You will practice using pipelines and hyperparameter optimisation to train a classification model, evaluate the classifier in a white box fashion, and become familiar with handling multi-dimensional data.

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