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For this assignment, you will carry out a binary classification task, and write a report on this. The data come from photos, and your task
For this assignment, you will carry out a binary classification task, and write a report on this.
The data come from photos, and your task is to come up with a machine learning method for classifying the photos according to whether their content is happy or sad. The data you are given for each photo consists of features. of these were extracted from a deep Convolutional Neural Network CNN and the remaining are gist features You are given all these features as a dimensional array, so you will not be performing any feature extraction on raw images.
There are two files of training data. The first contains samples with all the data present no missing or null values The second contains samples, which have some missing data, as indicated by a NaN not a number The training data have class labels, for happy, and for sad. In addition, there is also a confidence label for each sample. The class labels were assigned based on decisions from people viewing the photos. When they all agreed, the class label could be considered certain, and a confidence of was written down. If they didn't all agree, then the classification decided on by the majority was assigned, but with a confidence of only
There is one file of test data, containing samples. You must generate predictions for the class labels of these data. Note that, as with the second training set, the samples in the test data set contain some missing features.
Your job is to obtain the best predictions you can, and to justify your methods. You should provide reasons for which classifier or combination of classifiers you use, how you do model selection trainingvalidation split or cross validation and how you handle the specific issues with these data large number of features, missing data, the presence of confidence labels for the classes of the training data We value creative approaches!
You may make use of any classifier, such as: singlelayer perceptron, multilayer perceptron, SVM random forest, logistic regression, etc. You are not required to code classifiers from scratch, and you can use any machine learning toolbox you like, such as scikitlearn
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