Accelerate Panel Data Analysis with Automated Feature Discovery and Self Joins

Accelerate Panel Data Analysis with Automated Feature Discovery and Self Joins

Table of Contents

• Introduction
• Understanding Model Accuracy
• The Role of Automation in Model Accuracy
• What is Panel Data?
• Examples of Panel Data
• Understanding Flight Delays and Airlines
• Classification vs Regression Projects
• Options for Paneling Flight Delay Data
• Improving Model Accuracy with Time-Aware Feature Engineering
• The Self-Join Technique: Explained
• Preventing Target Leakage in Self-Join Technique
• Accessing the Notebook for Self Joins
• Importing the Notebook into DataRobot
• Exploratory Data Analysis
• Setting Up the Self-Join Process
• Defining Self-Join Rules
• Setting Feature Discovery Settings
• Bringing it all Together: Relationship Config
• Starting the Project in DataRobot
• Feature Engineering and Reduction
• Analyzing the Top Performing Model
• Trimming Down Features for Better Accuracy
• Understanding Auto-Generated Feature Names

Improving Model Accuracy with the Self-Join Technique

When it comes to improving the accuracy of our models, there is often a trade-off between time and accuracy. Spending more time on a model can certainly lead to increased accuracy, but it is not always feasible or efficient. In this article, we will explore the use of DataRobot's advanced automation features to achieve higher accuracy without investing additional time.

Understanding Model Accuracy

Before diving into the self-join technique, it is important to understand the concept of model accuracy. Model accuracy refers to how effectively a model predicts or classifies data. The higher the accuracy, the more reliable and trustworthy the model's output. However, improving accuracy requires careful consideration of various factors, including the available dataset, feature engineering techniques, and the model's complexity.

The Role of Automation in Model Accuracy

DataRobot's advanced automation features play a crucial role in improving model accuracy. By automating the process of feature engineering and reduction, DataRobot saves valuable time for data scientists. Manual feature engineering involves creating aggregated features Based on subject matter expertise, but this can be a time-consuming process. With DataRobot's automation, these features can be automatically generated, avoiding the need for manual coding and allowing data scientists to focus on the modeling step.

What is Panel Data?

Panel data is a subset of longitudinal data where observations are collected for the same subject over time. While panel data is commonly associated with econometrics, it can be applied to various domains, including education, local government, and even flight delays and airlines. Panel data allows us to analyze the behavior and Patterns of entities over time, providing insights into the factors that contribute to a specific outcome.

Examples of Panel Data

To better understand panel data, let's explore some examples. For instance, in education, we can track the exam scores of the same student over time, which helps identify trends and patterns in their academic performance. In local government, monitoring the weather of a specific city over time can provide valuable information for climate prediction. In our case, we will be focusing on flight delays and airlines, aiming to predict whether a flight's takeoff will be delayed by more than 30 minutes.

Understanding Flight Delays and Airlines

Flight delays are a common concern for both passengers and airlines. By analyzing the factors contributing to flight delays, we can gain insights into potential causes and find ways to mitigate them. In this classification project, we will utilize machine learning models to predict whether a flight's takeoff will be delayed by more than 30 minutes. Additionally, we have the option to approach this as a regression project, predicting the delay in minutes.

Options for Paneling Flight Delay Data

In panel data analysis, we have multiple options for paneling our flight delay data. We can choose to panel the data based on the Carrier Code, Origin Airport, or Destination Airport. Each approach provides a different perspective on the data and may yield different insights. In our example, we will explore all three options, creating a comprehensive panel data analysis.

Improving Model Accuracy with Time-Aware Feature Engineering

To truly improve model accuracy, we need to leverage time-aware feature engineering. By looking at the historical data of flights and considering specific time periods, we can Create aggregated features that capture the temporal patterns and trends. DataRobot's automation allows us to generate these time-aware features efficiently and automatically, reducing the time and effort required from data scientists.

The Self-Join Technique: Explained

The self-join technique is a powerful approach to panel data analysis. By joining a dataset onto itself, we can incorporate crucial information from past observations to improve predictions. In our case, we will perform self-joins based on the Origin Airport and the Carrier Code. This allows us to hypothesis that the recent history of the Origin Airport and Carrier Code may impact the likelihood of flight delays.

Preventing Target Leakage in Self-Join Technique

When performing self-joins, it is crucial to avoid target leakage or future leakage. Target leakage occurs when we inadvertently include information in the model that would not be available at the time of prediction. To prevent this, we need to carefully handle the time intervals for aggregation and ensure that only past information is considered. Understanding and implementing the necessary precautions can lead to more accurate and reliable predictions.

Accessing the Notebook for Self Joins

To explore the self-join technique in Detail, we will be using a notebook available on the DataRobot Community AI accelerators GitHub. The notebook provides an end-to-end solution for this use case, including the necessary data and step-by-step instructions. For a seamless experience, the notebook can be directly imported into DataRobot Notebooks, allowing easy collaboration and execution of the provided code.

Importing the Notebook into DataRobot

To import the self-join notebook into DataRobot Notebooks, You can simply copy the raw JSON file's URL from the GitHub repository. In DataRobot Notebooks, use the "Upload a notebook from a URL" feature and paste the copied URL. This will import the notebook directly into DataRobot Notebooks, allowing you to run the code and follow along with the steps. As the self-join process may take a few hours to complete, it is advisable to access the completed notebook in GitHub to review the results.

Exploratory Data Analysis

Before delving into the self-join technique, the notebook begins with exploratory data analysis. This step helps us understand the dataset's characteristics, distributions, and the target variable we aim to predict. While we won't cover the specific details of this analysis in this article, it sets the foundation for the subsequent steps and demonstrates the initial insights obtained from the data.

Setting Up the Self-Join Process

To utilize the self-join technique, we need to set up the necessary configurations. This includes defining self-join rules, establishing relationships between datasets, and setting feature discovery settings. These configurations allow DataRobot to understand how the datasets should be joined and what aggregations or calculations need to be performed. By carefully defining these configurations, we can ensure the accuracy and reliability of the self-join technique.

Defining Self-Join Rules

With the self-join process, we define rules that dictate how the datasets should be joined. This involves identifying unique identifiers and specifying the date-time feature for linking the data. By defining self-join rules, we can define the relationships between the datasets and enable DataRobot to perform the necessary aggregations and calculations. This step is crucial for ensuring the correct integration of the data and achieving accurate predictions.

Setting Feature Discovery Settings

In the self-join process, DataRobot performs feature discovery to identify Relevant features for modeling. We can specify the desired feature discovery settings to control the Type of aggregations and calculations performed. By fine-tuning these settings, we can focus on the features that contribute the most to the accuracy of our models. DataRobot offers various feature discovery options, allowing us to customize the feature engineering process according to our specific requirements.

Bringing it all Together: Relationship Config

To enable the self-join technique, we need to bring together the data definitions, relationships, and feature discovery settings. This is done through the relationship configuration, which captures all the necessary information for DataRobot's self-join process. Once this configuration is complete, you can switch to the DataRobot GUI to Visualize the relationships between the datasets. This Diagram provides a clear overview of the data integration and allows us to validate the relationships before proceeding.

Starting the Project in DataRobot

Once we have set up the relationships and feature configurations, we can start the project in DataRobot. Starting the project initiates the model competition process, where various modeling techniques will be applied to the data. In the project setup, we define the classification target, the optimization metric (such as log loss or AUC), and the desired number of workers. DataRobot's automation will then take over to create and evaluate multiple models, enabling us to identify the top-performing model.

Feature Engineering and Reduction

As the project progresses, DataRobot performs feature engineering and reduction to identify the most relevant features. By leveraging the self-join technique, we generate a substantial number of new features. However, not all of these features may contribute significantly to the model's accuracy. DataRobot's automation evaluates each feature's relevance and reduces the feature set to improve model efficiency and prevent overfitting. Understanding the feature engineering and reduction process allows us to fine-tune the model and improve its accuracy.

Analyzing the Top Performing Model

Once the project completes, we can analyze the top-performing model to understand its features and performance. DataRobot provides a comprehensive leaderboard that ranks the models based on their performance metrics, such as log loss or AUC. By examining the top-performing model, we can gain insights into the important features driving its accuracy. This analysis helps us understand the model's behavior and provides valuable information for subsequent iterations and improvements.

Trimming Down Features for Better Accuracy

During the analysis of the top-performing model, we may decide to trim down the feature set to improve model accuracy further. By focusing on the most informative features, we can potentially enhance the model's performance. DataRobot allows us to compare the impact of different feature sets on log loss or AUC. By carefully selecting features that contribute significantly to the predictions, we can optimize the model's accuracy while keeping it manageable and interpretable.

Understanding Auto-Generated Feature Names

Throughout the self-join technique and feature engineering process, DataRobot automatically generates feature names. These names offer human readability and help us understand the origin and purpose of each feature. By examining the feature lineage, we can Trace back each feature's creation and understand the underlying calculations and aggregations. Understanding these auto-generated feature names provides deeper insights into the modeling process and aids in effective feature selection and interpretation.

In conclusion, the self-join technique, in combination with DataRobot's automation features, provides a powerful approach to improving model accuracy. By leveraging panel data, time-aware feature engineering, and automated model competition, data scientists can achieve accurate predictions without investing excessive time and effort. This technique is applicable in various domains beyond flight delays and airlines, offering valuable insights into temporal patterns and trends. Through a code-first experience in DataRobot Notebooks, data scientists can collaborate, analyze models, and optimize accuracy efficiently.