📝 Summary
-Content: Asking various questions about model (algorithm) selection in a Kaggle competition
-Conclusion: I now understand the overall picture of model selection. Next, I’ll move on to data understanding!
🎯 Goal
This time, I will ask Copilot various questions about model selection in the competition and clear up my doubts!
- Unfinished tasks around model selection
- Organizing prompts for next time
- Characteristics of each model
- Performance differences between models
- Performance gap compared to the top teams
I will ask Copilot these questions.
📍 Current Situation and Today’s Challenge
- This is my first time joining a competition in collaboration with AI❗
- With an ensemble of CatBoost, LightGBM, and XGBoost, I’m currently in the top 20%
- But I still don’t understand the characteristics and proper use of each model
This time’s wall is:
“Did I leave anything undone in model selection?”
“Is this approach to model selection okay?”
I want to clear these two points.
💬 Log with Copilot
*Blue text is the input prompt to the AI.
I want to take on Kaggle with Copilot’s help.
With the code you made the other day, I managed to climb pretty high!
If there’s anything left undone around model selection that could have a big impact, please tell me!
📝Uploaded the previous Python code
Copilot’s answer
Nice, moving on to the data-understanding phase with that momentum is a great flow.
But looking at your current code, there are still several “high-impact improvement points” left in the model section.
🔥 High-impact unfinished points in model selection
1. CatBoost’s categorical handling is not being used
Right now, all features in train/test are treated as numeric.
But this competition has many categorical variables.
CatBoost is one of the strongest models for categorical handling, so simply passing categorical columns as-is can increase AUC.
What to do:
cat_features = [col for col in X.columns if X[col].dtype == 'object']
train_pool = Pool(X_trn, label=y_trn, cat_features=cat_features)
This is very effective.
2. LightGBM / XGBoost categorical encoding is not optimized
Right now, you’re passing pandas data directly, so they are likely being auto-converted to float.
For LightGBM:
- specify categorical_feature
- tune max_cat_threshold
- tune min_data_per_group
For XGBoost:
- enable_categorical=True
- tree_method='hist' is fine
This improves categorical handling.
3. Optuna weight optimization assumes “linear blending”
Linear blending is strong, but if model correlations are high, performance plateaus.
Next step:
✔ Meta-model (stacking)
Use OOF as features and train:
- Logistic Regression
- CatBoost
- LightGBM
This is often stronger than linear blending.
4. OOF distribution is not checked
If OOF distributions are skewed, Optuna may not optimize weights well.
Good things to do:
- Compare AUC of each model’s OOF
- Visualize correlation between OOFs
- Overlay ROC curves
This reveals “which models are truly complementary.”
Wait, was the standalone version already handling categorical features?
📝Uploaded the previous standalone CatBoost model code
Copilot’s answer
In conclusion, the standalone CatBoost code you sent was “almost fully handling categorical features.”
⇒ So that means the categorical handling was omitted when rewriting the standalone version into the ensemble version!
Last time, including debugging LightGBM, I had Copilot output several large code blocks before generating the ensemble version, so the conversation became long and “omissions” likely occurred.
⇒ I probably need to shorten the code or restart the thread once the conversation gets long. This might be a limitation of free AI tools…
Then, could you output an improved ensemble version where CatBoost, LightGBM, and XGBoost all support categorical features? I’ll send the base code again for reference.
📝Uploaded the previous ensemble version code
Copilot’s answer
🎯 Improvement points (common to all models)
✔ CatBoost
- Add Pool(..., cat_features=cat_cols)
- Extract categorical columns based on dtype (object / category)
✔ LightGBM
- Pass categorical_feature=cat_cols to Dataset
- LightGBM requires categorical columns to be converted to int codes
→ convert pandas category → int codes
✔ XGBoost
- Add enable_categorical=True
- XGBoost also requires int codes
→ convert category → int codes
🧠 Important policy
For categorical columns:
- CatBoost: pass them as-is
- LightGBM / XGBoost: convert to int codes
This is the most stable and strong approach.
🐈CatBoost 🌿LightGBM 🔥XGBoost
Categorical-support ensemble version (improved)
# =========================
# 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 lightgbm as lgb
import xgboost as xgb
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')
train['Heart Disease'] = train['Heart Disease'].map({'Presence': 1, 'Absence': 0})
features = train.columns.drop(['id', 'Heart Disease'])
X = train[features].copy()
y = train['Heart Disease']
X_test = test[features].copy()
# =========================
# 1-2. Extract categorical columns
# =========================
cat_cols = X.select_dtypes(include=['object', 'category']).columns.tolist()
# Convert categorical columns to int codes for LightGBM / XGBoost
X_lgb = X.copy()
X_test_lgb = X_test.copy()
for col in cat_cols:
X_lgb[col] = X_lgb[col].astype('category').cat.codes
X_test_lgb[col] = X_test_lgb[col].astype('category').cat.codes
# XGBoost uses the same data
X_xgb = X_lgb
X_test_xgb = X_test_lgb
kf = KFold(n_splits=5, shuffle=True, random_state=42)
# =========================
# 2. Best params for each model
# =========================
cat_params = {
'depth': 4,
'learning_rate': 0.13052054926825793,
'l2_leaf_reg': 2.6990678382790696,
'bagging_temperature': 0.30742776239026104,
'border_count': 225,
'loss_function': 'Logloss',
'eval_metric': 'AUC',
'iterations': 1000,
'random_seed': 42,
'verbose': 0
}
lgb_params = {
"objective": "binary",
"metric": "auc",
"verbosity": -1,
"boosting_type": "gbdt",
"random_state": 42,
"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
}
xgb_params = {
"objective": "binary:logistic",
"eval_metric": "auc",
"tree_method": "hist",
"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,
"enable_categorical": True
}
# =========================
# 3. Create OOF and test predictions for each model
# =========================
oof_cat = np.zeros(len(train))
oof_lgb = np.zeros(len(train))
oof_xgb = np.zeros(len(train))
test_cat = np.zeros(len(test))
test_lgb = np.zeros(len(test))
test_xgb = np.zeros(len(test))
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]
# CatBoost (categorical columns as-is)
train_pool = Pool(X_trn, label=y_trn, cat_features=cat_cols)
val_pool = Pool(X_val, label=y_val, cat_features=cat_cols)
test_pool = Pool(X_test, cat_features=cat_cols)
model_cat = CatBoostClassifier(**cat_params)
model_cat.fit(train_pool, eval_set=val_pool, use_best_model=True)
oof_cat[val_idx] = model_cat.predict_proba(val_pool)[:, 1]
test_cat += model_cat.predict_proba(test_pool)[:, 1] / kf.n_splits
# LightGBM (categorical columns as int codes)
X_trn_lgb, X_val_lgb = X_lgb.iloc[trn_idx], X_lgb.iloc[val_idx]
train_set = lgb.Dataset(X_trn_lgb, y_trn, categorical_feature=cat_cols)
val_set = lgb.Dataset(X_val_lgb, y_val, categorical_feature=cat_cols)
model_lgb = lgb.train(
lgb_params,
train_set,
valid_sets=[val_set],
num_boost_round=2000,
callbacks=[lgb.early_stopping(100), lgb.log_evaluation(period=0)]
)
oof_lgb[val_idx] = model_lgb.predict(X_val_lgb)
test_lgb += model_lgb.predict(X_test_lgb) / kf.n_splits
# XGBoost (categorical columns as int codes)
X_trn_xgb, X_val_xgb = X_xgb.iloc[trn_idx], X_xgb.iloc[val_idx]
dtrain = xgb.DMatrix(X_trn_xgb, label=y_trn)
dval = xgb.DMatrix(X_val_xgb, label=y_val)
dtest = xgb.DMatrix(X_test_xgb)
model_xgb = xgb.train(
xgb_params,
dtrain,
num_boost_round=2000,
evals=[(dval, "valid")],
early_stopping_rounds=100,
verbose_eval=False
)
oof_xgb[val_idx] = model_xgb.predict(dval)
test_xgb += model_xgb.predict(dtest) / kf.n_splits
# =========================
# 4. Optimize weights with Optuna
# =========================
def objective_w(trial):
w1 = trial.suggest_float("w_cat", 0.0, 1.0)
w2 = trial.suggest_float("w_lgb", 0.0, 1.0)
w3 = trial.suggest_float("w_xgb", 0.0, 1.0)
w_sum = w1 + w2 + w3 + 1e-9
oof_blend = (w1 * oof_cat + w2 * oof_lgb + w3 * oof_xgb) / w_sum
return roc_auc_score(y, oof_blend)
study_w = optuna.create_study(direction="maximize")
study_w.optimize(objective_w, n_trials=50)
print("Best weights:", study_w.best_trial.params)
best_w = study_w.best_trial.params
w1, w2, w3 = best_w["w_cat"], best_w["w_lgb"], best_w["w_xgb"]
w_sum = w1 + w2 + w3 + 1e-9
# =========================
# 5. Ensemble test predictions with best weights
# =========================
test_pred = (w1 * test_cat + w2 * test_lgb + w3 * test_xgb) / w_sum
submission = pd.DataFrame({
"id": test["id"],
"Heart Disease": test_pred
})
submission.to_csv("submission.csv", index=False)
print("Created submission.csv.")
Here is the improved ensemble version code
Score: 0.95374
Rank: 707/4370
⇒ Same performance as last time⁉️
Thank you!
So, if I want you to generate the same model for a different competition in one shot,
what kind of prompt should I give you?
Could you output the prompt in a code block?
Copilot’s answer
📌 “Prompt for generating the ensemble version in one shot”
Just paste the following as-is.
Using the uploaded train.csv and sample_submission.csv,
please generate a complete Python script that trains
CatBoost / LightGBM / XGBoost with categorical support,
creates OOF predictions, optimizes weights with Optuna,
and outputs submission.csv.
Requirements:
1. Automatically detect categorical columns using object / category dtype
2. Pass categorical columns directly to CatBoost
3. Convert categorical columns to int codes for LightGBM and XGBoost
4. Use KFold (5 splits, shuffle=True, random_state=42)
5. Create OOF and test predictions for each model
6. Optimize weights of the 3 models using Optuna
7. Ensemble test predictions with optimized weights and create submission.csv
8. Code must be self-contained in one file and runnable as-is
Please generate code that fully adheres to this specification.
Since we're here, could you explain the rough internal workings and characteristics of the three models in an easy-to-understand way?
Copilot’s answer
🐈⬛ CatBoost
■ In one sentence
“A genius that excels at categorical data with almost no effort required.”
■ Core idea
- Uses a unique method called “Ordered Boosting”
- Automatically performs target encoding for categorical variables
- Less prone to overfitting (uses ordered statistics)
■ Strengths
- Very strong with categorical columns passed as-is
- Almost no preprocessing needed
- Hyperparameters are relatively stable
- Especially strong on small to medium tabular datasets
- Handles missing values automatically
■ Weaknesses
- Slower than LightGBM on large datasets
- Can be heavy without GPU
■ Best use cases
- Many categorical columns
- Want minimal preprocessing
- Small to medium tabular data
🌿 LightGBM
■ In one sentence
“A fast, high-accuracy, all-purpose model widely used in production.”
■ Core idea
- Leaf-wise tree growth
- Fast implementation of gradient boosting
- Histogram-based computation
■ Strengths
- Extremely fast
- Handles large datasets well
- Highly flexible hyperparameters
- Excellent balance of speed and accuracy
- Can handle categorical columns (with int codes)
■ Weaknesses
- Can overfit depending on hyperparameters
- Categorical handling is not as strong as CatBoost
- Sometimes requires preprocessing
■ Best use cases
- Large datasets
- Fast experimentation
- Stable production models
🔥 XGBoost
■ In one sentence
“A reliable veteran with strong stability and regularization.”
■ Core idea
- Classic gradient boosting implementation
- Strong L1/L2 regularization
- Depth-wise tree growth
■ Strengths
- Consistently strong performance
- Less prone to overfitting (strong regularization)
- Excellent GPU support
- Long track record in competitions and industry
■ Weaknesses
- Often slower than LightGBM
- Categorical support is newer and not as strong as CatBoost
- Hyperparameter tuning can be tricky
■ Best use cases
- Need a stable baseline
- Want strong regularization
- Mostly numerical features
🎯 Summary of differences
CatBoost: Best for categorical-heavy datasets
LightGBM: Fast, versatile, great for large datasets
XGBoost: Stable, regularized, strong with numerical data
This time, even though the dataset contained categorical data, CatBoost didn’t outperform the others. Surprisingly, all three models had very similar performance. Why do you think the performance difference was so small?
Copilot’s answer
🔍 Why didn’t CatBoost stand out, and why were the three models so close?
1. The categorical columns didn’t contain much “information”
CatBoost shines when:
- Many unique categories
- Complex category interactions
- Target encoding is powerful
- Hard-to-numericalize categories exist
In this competition:
- Categorical columns existed but weren’t complex
- Low cardinality (2–5 categories)
- Numerical features dominated
So LightGBM/XGBoost could express the patterns just as well.
2. Numerical features were more important
If numerical features dominate:
- CatBoost’s categorical advantage shrinks
- Tree models behave similarly
- Performance converges
This dataset had many strong numerical features:
Age, Blood Pressure, Cholesterol, Blood Sugar, etc.
3. Categorical columns were “almost one-hot-like”
If categories are:
- Binary (Yes/No)
- 3-level (Low/Medium/High)
- Small groups
Then CatBoost’s advanced encoding doesn’t create a big gap.
LightGBM/XGBoost can handle them fine with int codes.
4. Hyperparameters converged to similar model complexity
Your tuned parameters:
- CatBoost depth=4
- LightGBM max_depth=3
- XGBoost max_depth=3
Shallow trees:
- Reduce overfitting
- Make models behave similarly
- Reduce performance differences
5. The dataset had low noise and simple structure
If:
- Key features are clear
- Noise is low
- Nonlinearity is mild
- Few interactions
Then all tree models converge to similar performance.
6. OOF correlations were likely high
High OOF correlation means:
- Models learn the same patterns
- Data structure is simple
- Ensemble gains are limited
🎯 Summary
This dataset was not the type where CatBoost’s categorical strength creates a big advantage.
By the way, the top AUC was 0.95408, and ours was 0.95374. How should we interpret this? Does it mean we’re almost perfectly predicting? Or is there still room for improvement?
Copilot’s answer
This difference becomes more meaningful as you gain more competition experience.
The 0.95408 vs 0.95374 gap is extremely insightful.
🎯 Conclusion: The models are “almost equal,” but there is still room to improve
Difference: 0.00034
In AUC terms, this is tiny.
But in Kaggle, tiny differences can shift rankings dramatically.
Meaning:
- The model is near optimal
- But competition-wise, there is still room to improve
🧠 AUC above 0.95 is the “saturation zone”
Improvements become:
- 0.0001 to 0.001 increments
In this zone:
- Feature engineering
- Preprocessing
- External data
- Ensemble tricks
- Seed averaging
- Stacking
These matter more than model choice.
🔍 Is this difference “just noise”?
✔ Model-wise → almost noise
The dataset structure made model differences small.
🔍 Competition-wise?
✔ In Kaggle → 0.0003 can decide rankings
🔥 Where is the improvement potential?
Not in the model, but in the data.
1. Interaction features
2. Log/Box-Cox transforms
3. Category regrouping
4. Seed averaging
5. Stacking (2nd stage model)
🌟 Your current AUC 0.95374 means:
- You’ve extracted almost all model performance
- Ensemble is correct
- You’re ready for the data-understanding phase
From here on, it’s a “data craftsmanship” phase.
So, for the next competition, I want to focus heavily on data understanding!
📊 Results (Score & Ranking)
Score: 0.95374 (Previous: 0.95374)
Rank🏆: 707/4370 (Previous: 377/2514)
No performance improvement this time
📚 Insights
1. CatBoost, LightGBM, and XGBoost are all gradient-boosted decision trees (GBDT)
2. With the current free AI, long code outputs (5+ times) may cause omissions
3. I now understand the overview of model selection; next time I’ll focus on data understanding
🔮 Next
I want to start again from building a base model in the new competition, Playground Episode 3.
English translation by Copilot.
