Predictive Analytics: Generalized Linear Regression by Ricky Ho.
From the post:
In the previous 2 posts, we have covered how to visualize input data to explore strong signals as well as how to prepare input data to a form that is situation for learning. In this and subsequent posts, I’ll go through various machine learning techniques to build our predictive model.
- Linear regression
- Logistic regression
- Linear and Logistic regression with regularization
- Neural network
- Support Vector Machine
- Naive Bayes
- Nearest Neighbor
- Decision Tree
- Random Forest
- Gradient Boosted Trees
There are two general types of problems that we are interested in this discussion; Classification is about predicting a category (value that is discrete, finite with no ordering implied) while Regression is about predicting a numeric quantity (value is continuous, infinite with ordering).
For classification problem, we use the “iris” data set and predict its “species” from its “width” and “length” measures of sepals and petals. Here is how we setup our training and testing data.
Ricky walks you through linear regression, logistic regression and linear and logistic regression with regularization.