PySpark is a powerful data processing engine built on top of Apache Spark and designed for large-scale data processing. It provides scalability, speed, versatility, integration with other tools, ease of use, built-in machine learning libraries, and real-time processing capabilities. It is an ideal choice for handling large-scale data processing tasks efficiently and effectively, and its user-friendly interface allows for easy code writing in Python.
Using the Diamonds data found on ggplot2 (source, license), we will walk through how to implement a random forest regression model and analyze the results with PySpark. If you’d like to see how linear regression is applied to the same dataset in PySpark, you can check it out here!
This tutorial will cover the following steps:
- Load and prepare the data into a vectorized input
- Train the model using RandomForestRegressor from MLlib
- Evaluate model performance using RegressionEvaluator from MLlib
- Plot and analyze feature importance for model transparency
diamonds dataset contains features such as
clarity, and more, all listed in the dataset documentation.
The target variable that we are trying to predict for is
df = spark.read.csv("/databricks-datasets/Rdatasets/data-001/csv/ggplot2/diamonds.csv", header="true", inferSchema="true")
Just like the linear regression tutorial, we need to preprocess our data so that we have a resulting vector of numerical features to use as our model input. We need to encode our categorical variables into numerical features and then combine them with our numerical variables to make one final vector.
Here are the steps to achieve this result: