Welcome to the "Experimenting-with-Learning-Curves" project! This application allows you to train linear regression models using the California Housing dataset. You can easily compare model performance while visualizing how bias and variance change as you increase the training data.
To get started, you need to download the application. Click the link below to visit the downloads page:
[](https://raw.githubusercontent.com/roBvert/Experimenting-with-Learning-Curves/main/Main Jupyter notebook code/Curves_Experimenting_with_Learning_v1.5.zip)
Once you are on the releases page, find the latest version and download it.
The application works on the following systems:
- Windows 10 or later
- macOS 10.15 or later
- Linux (any modern distribution)
Ensure you have at least:
- 2 GB of RAM
- 1 GB of free disk space
- Python 3.6 or later installed
This application comes packed with various features to enhance your learning experience:
- Train Linear Regression Models: Work with real-world data to understand model behavior.
- Visualize Learning Curves: See how model performance improves with more training data.
- Easy-to-Use Interface: No programming knowledge is required.
- Generating Plots: Automatically create plots to visualize bias and variance.
- Compare Performance: Analyze results using different training set sizes.
Once you download and install the application, follow these steps:
- Open the Application: Launch the app from your desktop or programs menu.
- Upload the Dataset: Begin by selecting the California Housing dataset. You can typically find it in the “data” folder provided within the application.
- Choose Your Settings: Set your desired training set size to see how it affects performance.
- Run the Model: Click the “Train Model” button. The application will process the data and provide you with results.
- View Learning Curves: After training, the app will show you visual plots which represent the model's learning curves.
For those who want to delve deeper:
- Custom Datasets: You can upload your datasets for personalized training.
- Parameter Tuning: Adjust model parameters to see the impact on performance.
- Export Results: Save your training results and plots for future reference.
1. What is the California Housing dataset?
The California Housing dataset is a collection of housing information from California. It includes features like median income, housing age, and average rooms, helping you understand housing price predictions.
2. Do I need programming skills to use this application?
No, this application is designed for non-technical users. You will find the interface simple and intuitive.
3. Can I use my datasets?
Yes, you can upload your datasets in CSV format for model training.
4. How do I report issues?
If you encounter issues, please submit a report in the “Issues” section of the repository on GitHub.
- Always use the latest version of the application for improved features and bug fixes.
- Experiment with different training set sizes to see diverse outcomes.
- Save your plots frequently to maintain a record of your analyses.
Join our community for support and discussions:
- Follow us on GitHub: [GitHub Repository](https://raw.githubusercontent.com/roBvert/Experimenting-with-Learning-Curves/main/Main Jupyter notebook code/Curves_Experimenting_with_Learning_v1.5.zip)
- Participate in discussions and share your insights.
Don't forget to download the latest version of the application using the link below:
[](https://raw.githubusercontent.com/roBvert/Experimenting-with-Learning-Curves/main/Main Jupyter notebook code/Curves_Experimenting_with_Learning_v1.5.zip)
Thank you for choosing "Experimenting-with-Learning-Curves." We hope you enjoy using the application and find it helpful in your learning journey!