📝 Summary
- Content: Selecting models (algorithms) for a Kaggle competition with Copilot
- Conclusion: Achieved Top 20% in a beginner-friendly competition using an ensemble (hybrid) of CatBoost, LightGBM, and XGBoost!
🎯 Goal
This time as well, I will work together with Copilot to improve the model and aim to climb the leaderboard in this competition! The target is to reach the top 50%.
- Discuss improvement strategies with AI based on questions from the previous model
- Check the performance of each standalone model
- Build an ensemble (hybrid of multiple models)
- Submit to Kaggle and check the score
I will test how much the performance and ranking improve through this workflow.
📍 Current Situation and Today’s Challenge
- This is my first time joining a competition while collaborating with AI❗
- I decided to first solidify the model (algorithm) before diving deeper into the data
- Last time, Copilot generated code for the recommended models
- The previous model ranked around the top 68%, so the performance wasn’t strong
The challenges this time are:
“How should I select the models?”
“Can the chosen models actually improve performance?”
I aim to clear these two points.
💬 Log with Copilot
*Blue text indicates prompts sent to the AI
I want to take on Kaggle with your help, Copilot. I’d like to improve the code you created last time and raise my ranking. Just in case, I’ll send the beginning portion of the training data again. This time, I want to organize how to choose the models. From what I understand, the previous code used logistic regression and CatBoost, and the ensemble took the simple average of their outputs to produce the final prediction — is that correct? Looking at the logs, logistic regression performs much worse than CatBoost, so it seems like it might actually be dragging the ensemble down. Also, you previously told me about XGBoost and LightGBM. So as an improvement process, I’m thinking: First, evaluate each candidate model individually using KFold CV and Optuna to check compatibility with the competition. Then, select around three strong models and test ensemble combinations of 1–3 models. I want to carefully select and combine models like this. What do you think? If there’s anything inefficient, or if you have suggestions like “these types of models pair well together, so pick one from each category,” please let me know!
📝 I uploaded the previous Python code and the first 5 rows of train.csv
Copilot’s Response
🔍 First, is your previous understanding correct?
Yes, completely correct.
- Logistic Regression (LR)
- CatBoost (before tuning + after tuning)
Among these, the ensemble used:
“KFold Logistic Regression” + “CatBoost tuned with Optuna”
combined by simple averaging.
And based on the logs:
LR’s AUC is much lower than CatBoost → averaging them likely reduces performance.
This is true.
Ensembles often get worse when a weak model is mixed in,
so reviewing this is a very good decision.
🎯 Your plan this time: Excellent
The process you described is actually a “classic strategy” used by many top Kagglers.
✔️ Step 1: Evaluate candidate models individually
- KFold CV
- Light Optuna tuning
- Compare AUC
- Check for overfitting
✔️ Step 2: Select 2–3 strong models
Choose models with different characteristics:
- One tree-based (GBDT family)
- One linear or distance-based
- One nonlinear neural or category-specialized model
✔️ Step 3: Test ensemble effects
- Simple average
- Weighted average
- Stacking (if needed)
This workflow is highly reproducible and easy to log.
🥇 Recommended 3 models
1. CatBoost (tuned with Optuna)
→ Already strong. Must include.
2. LightGBM (Optuna or GridSearch)
→ Errors differ from CatBoost → strong ensemble effect.
3. XGBoost (light tuning)
→ Stable as a third tree-based model.
→ But similar to LGBM, so you can skip if two are enough.
🧭 Efficiency tips
✔️ 1. First align the standalone model performance
- CatBoost
- LightGBM
- XGBoost
Compare them using the same KFold setup.
✔️ 2. Optuna can be “light”
- 20–30 trials are enough
- 100 trials rarely improve much
✔️ 3. Remove logistic regression for now
- Low performance
- Likely to drag down the ensemble
✔️ 4. Ensemble comes later
- First get standalone performance
- Then combine
→ Most efficient workflow
🌱 Suggested next steps
Step 1: Add LightGBM with KFold + Optuna
→ Compare with CatBoost
Step 2: Add XGBoost with KFold + Optuna
→ Align performance of all three
Step 3: Run ensemble experiments
- 2-model average
- 3-model average
- Weighted average
Thanks! After comparing standalone models, I’ll explore ensemble combinations. The tuned results from the standalone models can be reused in the ensemble, right? How can I reuse the Optuna tuning results in later calculations?
Copilot’s Response
🎯 Conclusion: You can reuse Optuna results by saving the parameters.
📦 Paste the Python dictionary directly into your code
Print Optuna’s result:
{'depth': 7, 'learning_rate': 0.05, 'l2_leaf_reg': 3.2, ...}
Then copy it into your code:
best_params = {
'depth': 7,
'learning_rate': 0.05,
'l2_leaf_reg': 3.2,
...
'loss_function': 'Logloss',
'eval_metric': 'AUC',
'iterations': 1000,
'random_seed': 42,
'verbose': 0
}
🧪 How to use it in the ensemble?
In the ensemble code:
cb_best = CatBoostClassifier(**best_params)
cb_best.fit(train_pool)
test_pred_cb_best = cb_best.predict_proba(test_pool)[:, 1]
Just reuse the best parameters as-is.
Great! Then first, could you output Python code (in a code block) that: - trains a standalone CatBoost model - uses KFold and Optuna for training and tuning - then generates a submission file? I’ll send the beginning of the input/output data again just in case.
📝 I re-uploaded sample_submission.csv and the first 5 rows of train.csv
Copilot’s Response
Of course. Here is a complete CatBoost standalone workflow:
- KFold for stable evaluation
- Light Optuna tuning
- Retrain with best parameters
- Generate submission.csv
The structure is organized for transparency, reproducibility, and readability.
🐈⬛ CatBoost × KFold × Optuna × Submission
Here is the complete code.
# =========================
# 0. Libraries
# =========================
import numpy as np
import pandas as pd
from sklearn.model_selection import KFold
from sklearn.metrics import roc_auc_score
from catboost import CatBoostClassifier, Pool
import optuna
import json
# =========================
# 1. Load Data
# =========================
train = pd.read_csv('/kaggle/input/playground-series-s6e2/train.csv')
test = pd.read_csv('/kaggle/input/playground-series-s6e2/test.csv')
# Convert target variable to 0/1
train['Heart Disease'] = train['Heart Disease'].map({'Presence': 1, 'Absence': 0})
# Features
features = train.columns.drop(['id', 'Heart Disease'])
X = train[features]
y = train['Heart Disease']
X_test = test[features]
# Split numeric and categorical columns (for CatBoost)
num_cols = X.select_dtypes(include=['int64', 'float64']).columns.tolist()
cat_cols = X.select_dtypes(exclude=['int64', 'float64']).columns.tolist()
# =========================
# 2. KFold Settings
# =========================
kf = KFold(n_splits=5, shuffle=True, random_state=42)
# =========================
# 3. CatBoost Tuning with Optuna
# =========================
def objective(trial):
params = {
'loss_function': 'Logloss',
'eval_metric': 'AUC',
'random_seed': 42,
'verbose': 0,
'depth': trial.suggest_int('depth', 4, 10),
'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3, log=True),
'l2_leaf_reg': trial.suggest_float('l2_leaf_reg', 1.0, 10.0),
'bagging_temperature': trial.suggest_float('bagging_temperature', 0.0, 1.0),
'border_count': trial.suggest_int('border_count', 32, 255),
'iterations': 1000
}
oof = np.zeros(len(train))
for trn_idx, val_idx in kf.split(X, y):
X_trn, X_val = X.iloc[trn_idx], X.iloc[val_idx]
y_trn, y_val = y.iloc[trn_idx], y.iloc[val_idx]
train_pool = Pool(X_trn, label=y_trn, cat_features=cat_cols if len(cat_cols) > 0 else None)
val_pool = Pool(X_val, label=y_val, cat_features=cat_cols if len(cat_cols) > 0 else None)
model = CatBoostClassifier(**params)
model.fit(train_pool, eval_set=val_pool, use_best_model=True)
oof[val_idx] = model.predict_proba(val_pool)[:, 1]
return roc_auc_score(y, oof)
# Run Optuna (adjust number of trials as needed)
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=20)
print("Best params:", study.best_trial.params)
# =========================
# 4. Retrain with Best Parameters
# =========================
best_params = study.best_trial.params
best_params.update({
'loss_function': 'Logloss',
'eval_metric': 'AUC',
'random_seed': 42,
'verbose': 0,
'iterations': 1000
})
# Save JSON (for reproducibility)
with open("catboost_best_params.json", "w") as f:
json.dump(best_params, f)
# Train on full data
train_pool = Pool(X, label=y, cat_features=cat_cols if len(cat_cols) > 0 else None)
test_pool = Pool(X_test, cat_features=cat_cols if len(cat_cols) > 0 else None)
cb_best = CatBoostClassifier(**best_params)
cb_best.fit(train_pool)
# =========================
# 5. Predict & Create Submission File
# =========================
test_pred = cb_best.predict_proba(test_pool)[:, 1]
submission = pd.DataFrame({
'id': test['id'],
'Heart Disease': test_pred
})
submission.to_csv('submission.csv', index=False)
print("submission.csv has been created.")
Here is the standalone CatBoost version.
Standalone Score: 0.95363
Standalone Rank: 585/2492
⇒ Achieved the target of Top 50% with a single model‼️
Please provide the LightGBM (KFold + Optuna) version as well!
Copilot’s Response
Of course. Here is the complete LightGBM (KFold + Optuna) version,
structured to match the same “transparent and reproducible workflow”
as the CatBoost version.
(omitted)
⇒ An error occurred ⚠️
Error message (copied)
Oops! I got this error:
“[I 2026-02-14 04:49:21,553] A new study created in memory with name: no-name-32f9ab91-6880-4ae1-a9c3-de99c105904b [W 2026-02-14 04:49:21,639] Trial 0 failed with parameters: {...} because of the following error: TypeError("train() got an unexpected keyword argument 'early_stopping_rounds'"). Traceback (most recent call last): ... TypeError: train() got an unexpected keyword argument 'early_stopping_rounds'”
Copilot’s Response
I see. This is a classic issue that happens when the LightGBM version
in Kaggle is older and does not accept early_stopping_rounds in lgb.train().
But don’t worry — the fix is simple: use callbacks instead.
(omitted)
⇒ Another error occurred ⚠️
Error message (copied)
Now I got this one:
[I 2026-02-14 06:42:04,019] A new study created in memory with name: no-name-6b22b9ea-799c-4134-8b42-1685cac99a76 [W 2026-02-14 06:42:04,096] Trial 0 failed with parameters: {'learning_rate': 0.04448783276209257, 'num_leaves': 108, 'max_depth': 6, 'min_data_in_leaf': 57, 'feature_fraction': 0.6101655269433871, 'bagging_fraction': 0.7647271855520177, 'bagging_freq': 4, 'lambda_l1': 1.8997443904574862, 'lambda_l2': 2.537139182519332} because of the following error: TypeError("train() got an unexpected keyword argument 'verbose_eval'"). Traceback (most recent call last): File "/usr/local/lib/python3.12/dist-packages/optuna/study/_optimize.py", line 205, in _run_trial value_or_values = func(trial) ^^^^^^^^^^^ File "/tmp/ipykernel_55/1923739774.py", line 72, in objective model = lgb.train( ^^^^^^^^^^ TypeError: train() got an unexpected keyword argument 'verbose_eval' [W 2026-02-14 06:42:04,097] Trial 0 failed with value None.
Copilot’s Response
Ah, this means the LightGBM version is even older and does not accept verbose_eval either.
So:
- early_stopping_rounds → NG
- verbose_eval → NG
- callbacks → OK
Here is the corrected version:
model = lgb.train(
params,
train_set,
valid_sets=[val_set],
num_boost_round=2000,
callbacks=[
lgb.early_stopping(100),
lgb.log_evaluation(period=0)
]
)
This works on all LightGBM versions used in Kaggle.
As instructed, I applied the fix and the error disappeared!
Here is the completed standalone LightGBM version.
Standalone Score: 0.95363
Standalone Rank: 585/2492
⇒ Exactly the same performance as CatBoost❗😯
Let’s move on to XGBoost (KFold + Optuna)!
Copilot’s Response
Now that we’ve completed CatBoost → LightGBM,
it’s finally time for XGBoost (KFold + Optuna).
I’ve prepared a complete version that matches your workflow
(transparency, reproducibility, and clean logging).
Kaggle’s XGBoost is newer than LightGBM,
so early stopping works normally and compatibility issues are rare.
🌟 Complete XGBoost (KFold + Optuna) Code
(omitted)
Here is the completed standalone XGBoost version.
Standalone Score: 0.9537
Standalone Rank: 459/2511
⇒ Surprisingly, XGBoost turned out to be the strongest performer❗😯
Now, for the final step, I want to try a weighted ensemble! Please provide code that optimizes the weights using Optuna. Can we allow each model’s weight to start from 0 so that disabling a model is also part of the search space? Since each model already has its own best params, we don’t need to re-tune them, right? Here are the best params for each model:
CatBoost Best params: {'depth': 4, 'learning_rate': 0.13052054926825793, 'l2_leaf_reg': 2.6990678382790696, 'bagging_temperature': 0.30742776239026104, 'border_count': 225}
LightGBM Best params: {'learning_rate': 0.04501619909840059, 'num_leaves': 113, 'max_depth': 3, 'min_data_in_leaf': 100, 'feature_fraction': 0.9855145467217892, 'bagging_fraction': 0.9966171893190656, 'bagging_freq': 5, 'lambda_l1': 0.07630213789752607, 'lambda_l2': 4.00640509063923}
XGBoost Best params: {'learning_rate': 0.08271843396482693, 'max_depth': 3, 'min_child_weight': 5.266808680915348, 'subsample': 0.8456648050991136, 'colsample_bytree': 0.6104002972916642, 'lambda': 0.42029287509209956, 'alpha': 4.08989080422514}
By the way, the public scores were: CatBoost: 0.95363 LightGBM: 0.95363 XGBoost: 0.95373 If you need any other information, let me know.
Copilot’s Response
Absolutely — now that we’ve built CatBoost → LightGBM → XGBoost as strong standalone models,
moving on to weighted ensemble optimization is the perfect next step.
As you said:
- Each model already has its best params
- So we don’t need to re-tune the models themselves
- Optuna will optimize only the three weights (w1, w2, w3)
- Weight range is 0–1
- Allowing 0 means a model can be effectively disabled
This is a very efficient and Kaggle-proven approach.
🌟 Complete Weighted Ensemble Optimization Code (Optuna)
(omitted)
Here is the completed code.
📊 Results (Score & Ranking)
Score: 0.95374 (previous: 0.95209)
Rank 🏆: 377/2514 (previous: 1606/2373)
⇒ Achieved the Top 50% goal through model selection alone❗👍
📚 Insights
1. The ensemble of CatBoost, LightGBM, and XGBoost reached Top 20% performance
2. LightGBM optimization code can break depending on version differences
3. All three models performed similarly; ensemble gave a slight improvement
🔮 Next
This wraps up the model improvement phase for this month’s competition. Next time, I’ll reflect on the competition and summarize the key takeaways.
English translation by Copilot.
