davidmasse/ds-classification-intro-nyc-career-ds-062518

Classification

Thus far we have looked at regression and investigating how we can predict a continuous variable. Another category of problems in data science is to classify class membership. For example, we might want to predict whether or not someone has cancer, whether a video is appropriate for children, or what species an animal is. These problems are fundamentally different in their formulation because of the desired outputs.

The simplest case of this is a binary classification of 0 or 1. Typically 0 stands for 'not a member' while 1 stands for 'is a member'.

Here's some of the most important classification algorithms which we'll investigate in further depth in coming lessons:

• Logistic Regression
  • Calculates the probability of class membership using the sigmoid function. Then assigns class membmership.
• Decision Trees
  • Split the dataset feature by feature according to which feature will improve the accuracy of classification. For example, those with cholosterol higher than a certain value (to be specified using the tree algorithm) have risk for heart disease, those below that value don't incur a risk. Next, you can go onto another feature in the data set, such as age.
• Random Forests
  • An ensemble method for combining multiple decision trees.
• Support Vector Machines
  • Draws a decision plane seperating the classes. Maximizes the distance between datapoints and this plane.

Regression or Classification?

For each of the following scenarios determine whether you would apply a regression or classification algorithm.

Determining a child's future height.

#Regression or Classification?

Determining a child's future career.

#Regression or Classification?

Determining a car's brand.

#Regression or Classification?

Determining a car's year.

#Regression or Classification?

Determining a car's mileage.

#Regression or Classification?

Determining a flower's color.

#Regression or Classification?

Determining a flower's species.

#Regression or Classification?