### Spark & Python Notebooks V: Decision Trees & Model Selection

The fourth episode in our Spark series introduced **Logistic Regression** with MLlib. This new notebook explains how to use the library to build a **classifier** using **Decision Trees** on a large dataset. It also shows how powerful trees are in order to understand our data and even perform **model selection**.

### Spark & Python Notebooks IV: Logistic Regression & Model Selection

The third episode in our Spark series introduced the **MLlib** library and its **Statistics** and **Exploratory Data Analysis** capabilities. This fourth notebook explains how to use the library to build a **classifier** using **Logistic Regression** on a large dataset. It also describes two different approaches to **model selection**.

### Ridge regression model selection with R

If recently we used *best subset*
as a way of reducing the unnecessary model complexity, this time we are going
to use the *Ridge regression* technique.