Welcome to the House Prediction project, where we explore the powerful technique of model stacking to enhance the accuracy of our house price predictions. By combining the strengths of diverse base models, we aim to create an ensemble prediction that outperforms individual models alone.
In the quest to improve predictive accuracy, data scientists often turn to ensemble methods. Model stacking, also known as stacking ensemble, is one such approach where we combine predictions from multiple base models to create a stronger overall prediction.
To start, we choose a set of base models, each with its own unique characteristics. For this demonstration, we've selected the following base models:
- Random Forest Regressor
- Gradient Boosting Regressor
- K-Nearest Neighbors Regressor
- Decision Tree Regressor
By using a mix of models with different strengths and weaknesses, we aim to capture a wide range of patterns in the data.
The meta model, often referred to as the "stacking model," takes the predictions made by the base models and learns how to combine them effectively. In this case, we've chosen a Linear Regression model as the meta model. The meta model is trained to weigh the predictions of the base models in a way that maximizes overall predictive accuracy.
To implement the stacking ensemble, follow these steps:
- Define the base models, each with its unique configuration.
- Create an array to store the predictions made by the base models.
- Utilize K-Fold cross-validation to train the base models on different subsets of the training data and store their predictions in the array.
- Train the meta model using the predictions made by the base models as features and the actual target values as the target variable.
- Finally, use the ensemble of base models and the trained meta model to make predictions on the test data, resulting in a more accurate prediction.
By combining the predictions of various models through stacking, we can often achieve a more accurate prediction compared to relying solely on a single model. The diversity of models helps capture different nuances and patterns within the data.
Join us in further exploring and enhancing the model stacking approach. Learn how to contribute to the project and improve its capabilities.
This project is shared under the [License Name].
For inquiries, collaboration opportunities, or to learn more, feel free to reach out to [Your Contact Information].