What methods do top Kagglers employ for score gain?

Question

I’m currently taking a course on ML and part of my final grade is my position on a Kaggle competition (private one) regarding a classification task. The majority of groups tend to have a similar public score but there are some that spike through the rest. I am left wondering what techniques data scientists employ to increase their scores.

I am aware that a thoughtful pre-processing of the data is of chief importance as well as techniques such as grid search which help us find the best hyperparameters. This question is more about some out-of-the-box techniques that you employ when your aim is to increase your final score, for example, in the setting of a Kaggle competition.

Answer 1

Top Kagglers often employ a combination of traditional machine learning techniques and creative, out-of-the-box strategies to gain an edge in competitions. Here are some techniques:

1. Ensemble Methods

• Stacking/Blending: Combining predictions from multiple models. This involves training several models and then using another model to predict the target variable based on the predictions of the initial models.

• Bagging and Boosting: Techniques like Random Forest (bagging) and Gradient Boosting (boosting) can significantly improve performance.

2. Feature Engineering

• Creating new features: Deriving meaningful features from existing ones can provide valuable information to the model.

• Dimensionality Reduction: Techniques like Principal Component Analysis (PCA) or t-Distributed Stochastic Neighbor Embedding (t-SNE) can be useful.

3. Advanced Preprocessing

• Handling missing data: Creative ways to impute missing values.

• Outlier Detection and Treatment: Identifying and addressing outliers can improve model robustness.

4. Model Hyperparameter Tuning

• Bayesian Optimization: Efficiently searching hyperparameter space.

• Optuna, Hyperopt: Libraries for hyperparameter optimization.

5. Neural Architecture Search (NAS)

• Automated Model Design: Techniques to automatically search for the best neural network architecture.

6. Transfer Learning

• Using pre-trained models: Leveraging models trained on large datasets and fine-tuning them for the specific task at hand.

7. Data Augmentation

• Increasing Training Data: Applying transformations (rotations, flips, etc.) to artificially increase the size of the training dataset.

8. Domain-Specific Knowledge

• Understanding the problem domain: Incorporating domain-specific knowledge can lead to better feature engineering and model performance.

9. Advanced Modeling Techniques

• Neural Networks Architectures: Exploring different architectures, such as attention mechanisms, transformers, etc.
• XGBoost, LightGBM, CatBoost: Gradient boosting libraries that are often used for tabular data.

10. Time Series Techniques

• LSTM, GRU: For sequential data.

• Feature lagging and rolling statistics: Utilizing information from past time points.

11. Post-Processing

• Calibration: Adjusting predicted probabilities to improve the model's reliability.
• Threshold tuning: Adjusting the classification threshold based on the specific needs of the task.

12. Collaboration and Knowledge Sharing

• Participating in discussions: Sharing insights and learning from others on Kaggle forums.
• Team Collaboration: Forming teams to combine diverse skills and perspectives.

Answer 2

There are many methods that can be employed to increase your score on a Kaggle competition. Here are a few examples:

Advanced classification techniques: Using advanced classification techniques such as weighted average ensemble, stacked generalization ensemble, and power average ensemble can help improve your model's performance.

Data augmentation: Techniques such as random rotation, hue adjustments, saturation adjustments, contrast adjustments, brightness adjustments, cropping, and more can help improve your model's accuracy when dealing with image data.

Text augmentation: When dealing with text data, techniques such as exchanging words with synonyms, noising in RNN, and translating to other languages and back can help augment your training data and improve your model's performance.

External datasets: Using external datasets that contain variables that influence the predicate variable can help increase the performance of your model pre-processing.

I have even seen that in some cases, especially at the beginning of the site, teams took advantage of finding data leakage. For example, identifying customer's id that may potentially be linked to temporal patterns and thus model's prediction

This discussion may give you a fine summary of most of the techniques I mentioned above.

I hope it helps.