Wine-Regression

Problem Statement

We want to predict a "Alcohol Class(1..3)" for a set of features for a given wine example.

What we have

• This is an example of Multi-Class Classification Problem
• We have a dataset of Wine Recoginition from UCI Machine Learning Repo
• Here the set of attributes of the wine are the features(x1,x2,x3,...,x13)
• Classes of the wine are the labels(y)

Solution

• We have 3 classes(k=1..3) here, not 2 classes(1-0). Therefore need to use One-vs-All method.
• Randomize the wine dataset and store it in wine_random.mat file, becuase the wine dataset it too sorted
• We divide our dataset into "training set" and "test set"
• Use Logistic Regression Model to learn/train from the training set
• After training, apply the learned parameters to both the training set and test set, this is the testing phase.
• Calculate the max(h_x) for both training set & test set, store the indices of those max values
• Calculate the cost values for the training set & test set.
• If J{train}(theta) < J{test}(theta), we are probably overfitting, use Regularization
• Plot Learning Curve to learn if the model is undefitting/overfitting
• Compute Precision & Recall values for the test set to check for skewed classes

Software needed

• Octave v.4.4.1 or higher

TODO

• Add an input interface to take in wine examples from the command promt