Description
Objectives
1. Apply Kernel SVM and MLP classification algorithms to the fashion-MNIST dataset
2. Use k-fold cross validation to identify the best way to rescale and preprocess the data
3. Use k-fold cross validation to identify the parameters that optimize performance
(generalization) for each method
4. Compare the accuracy and identify correlation between the outputs of the two methods
Problem
For this homework, you will apply the following classification methods to the fashion-MNIST
classification data
1. Kernel Support Vector Machines
2. Multilayer Perceptrons
• Apply 4-fold cross-validation to the provided training data subset to train your classifiers and
identify their optimal parameters. In addition to the classifier’s parameters (e.g. regularization,
kernel, Number of layers/nodes, learning rate, etc.), you should also consider the following 4 ways
to preprocess and rescale the data:
a) No preprocessing
b) StandardScaler
c) RobustScaler
d) MinMaxScaler
• After fixing the classifiers’ parameters, apply each method to the provided testing data subset to
predict and analyze your results. Compare the accuracy obtained during training (average of the
cross-validation folds) to those of the test data and comment on the results (overfitting,
underfitting, etc.)
• Analyze the correlation between the output of the 2 classifiers by displaying the predict_proba of
SVM vs. predict_proba of MLP (using test data). Using these scatter plots (one per class), identify
(if available) the following 3 groups
• G-1: Samples that are easy to classify correctly by the SVM, but hard to classify by MLP
• G-2: Samples that are easy to classify correctly by the MLP, but hard to classify by SVM
• G-3: Samples that are hard to classify correctly by both methods
For each group, display few samples (as images) and identify any common features among them.
What to submit?
• A report that
o Describes your experiments, the parameters considered for each method, etc.
o Summarizes, explains (using concepts covered in lectures) and compares the
results (using plots, tables, figures)
• Do not submit your source code
• Your report needs to be a single file (MS Word or PDF)
• Your report cannot exceed 10 pages using a font of 12
• Assign numbers to all your figures/tables/plots and use these numbers to reference them in
your discussion
