Classification

[ronan-mch]

• Explain which classification problem you have chosen to solve.
• Apply at least three of the following methods: Decision Trees, Logistic/Multinomial Regression, K-Nearest Neighbors (KNN), Naı̈ve Bayes and Artificial Neural Networks (ANN). (Use cross-validation to select relevant parameters in an inner cross-validation loop and give in a table the performance results for the methods evaluated on the same cross-validation splits on the outer cross-validation loop, i.e. you should use two levels of cross-validation).
• For the models you are able to interpret explain how a new data observation is classified.
  (If you have multiple models fitted, (i.e., one for each cross-validation split) either focus on one of these fitted models or consider fitting one model for the optimal setting of the parameters estimated by cross-validation to all the data.)
• Statistically compare the performance of the two best performing models (i.e., use a paired t-test). Compare in addition if the performance of your models are better than simply predicting all outputs to be the largest class in the training data.