Predicting a qualitative response for an observation can be referred to as classifying that observation, since it involves assigning the observation to a category, or class.Just as in the regression setting, in the classification setting we have a set of training observations (x 1 , y 1 ), . . . , (x n , y n ) that we can use to build a classifier. We want our classifier to perform well not only on the training data, but also on test observations that were not used to train the classifier.Some examples of classification problems include:
- A person arrives at the emergency room with a set of symptoms that could possibly be attributed to one of three medical conditions. Which of the three conditions does the individual have?
- An online banking service must be able to determine whether or not a transaction being performed on the site is fraudulent, on the basis of the user’s IP address, past transaction history, and so forth.
- On the basis of DNA sequence data for a number of patients with and without a given disease, a biologist would like to figure out which DNA mutations are deleterious (disease-causing) and which are not.
Titanic: Machine Learning from Disaster is a knowledge competition on Kaggle. Many people started practicing in machine learning with this competition, so I am. This is a binary classification problem: based on information about Titanic passengers we predict whether they survived or not. General description and data are available on Kaggle. Titanic dataset provides interesting opportunities for feature engineering.
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classified data set from a company! They've hidden the feature column names but have given you the data and the target classes. We'll try to use KNN to create a model that directly predicts a class for a new data point based off of the features. Nb viewer