Abhishek-Thakur
This section of Kaggle's 30 Days of ML competition explores the advanced technique of stacking, which involves creating multiple models and using their predictions as features for a metamodel to generate new predictions. The presenter demonstrates how to implement this process in a Jupyter notebook using five different models and optimizing hyperparameters. They then train a final linear regression model on top of the level one predictions to get the final set of predictions, which they submit to Kaggle. The presenter encourages viewers to experiment with different stacking techniques but cautions against overfitting.
In this section of Kaggle's 30 Days of ML competition, they explore the concept of stacking. Stacking involves creating multiple models and generating predictions on both the validation and testing sets. The predictions are then used as features for a metamodel, which generates a new set of predictions that are used for the test set. Stacking is an advanced technique that can improve the accuracy of machine learning models, but it requires a lot of time and effort to implement.
In this section, the speaker discusses model stacking, which involves generating a final set of model predictions based on using the original features and creating another set of models using the first set of predictions as features. The speaker demonstrates how to code this process in a Jupyter notebook and advises viewers to use previous lectures to understand the process. The example relies on using five different models and generating useful features, which are the predictions from different level zero models. The final model is trained using the level one predictions, and the speaker warns that continuing to use models beyond this could lead to overfitting and producing poor results.
In this section, the presenter trains another XGBoost model on top of the predictions from level zero models to generate level one predictions. They then train random forest and gradient boosting regressors to generate a second and third set of level one predictions, respectively, and optimize the hyperparameters for each. Finally, they merge all three sets of level one predictions and train a simple linear regression model on top of them to get the final predictions, which are then dumped to a CSV file and submitted to Kaggle. The presenter notes that their rank is now fourth in the competition and encourages viewers to try stacking on their own.
In this section of Kaggle's 30 Days Of ML competition video, it is suggested to combine predictions with original features for model training. Additionally, it is advised to experiment with different stacking techniques but be cautious with folds so as to not overfit and obtain a good score on the leaderboard. Viewers are encouraged to use the same ports for cross validation and to like, subscribe, and share the video.
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