📝 Summary
-Content: Join a Kaggle competition with Copilot and improve the model
-Conclusion: It’s possible to generate working code even for elaborate models, but the competition is surprisingly tough
🎯 Goal
This time, together with Copilot, I aim to improve the model and climb the leaderboard in this competition!
- Discuss improvement strategies with AI
- Discuss priorities
- Generate improved models
- Submit to Kaggle and check the score
Through this whole flow, I’ll see how much the performance and ranking can be improved.
📍 Current Situation and Today’s Challenge
- This is my first time joining a competition in collaboration with AI❗
- In the previous post, Copilot generated the minimal code for me
- I (the author) don’t really know concretely what kind of improvements I should make
This time’s wall is:
“How can I improve the model’s performance while consulting with AI?”
“Can I generate complex code that actually improves performance?”
The goal is 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. I’d like to improve this code you made for me the other day and move up the leaderboard. Just in case, I’ll also send you the first few rows of the training data. Please tell me what improvement options there are.
And if you have “the ultimate strategy you can think of,” I’d love to hear it!
📝I uploaded the previous Python code and a file with only the top 5 rows cut out from 📝 train.csv.
Copilot’s answer
🔧 Improvement options (by stages)
1. Strengthening preprocessing
This part often directly affects the score.
- Refine encoding of categorical variables
- One-Hot Encoding
- Target Encoding
- Ordinal Encoding (when there is a meaningful order)
- Check handling of missing values
- This dataset has few missing values, but check just in case
- Handle outliers
- Outliers in Cholesterol or Max HR might matter
- Improve feature scaling
- Change StandardScaler → RobustScaler to increase robustness to outliers
2. Feature engineering
On Kaggle, this is often the most effective part.
- Interaction features
- Age × MaxHR
- Cholesterol / Age
- Nonlinear transformations
- Log transform of ST depression
- Domain-knowledge-based features
- Categorize “Age group”
- Binarize flags like “High BP flag”
- Dimensionality reduction such as PCA
- Works well with logistic regression
3. Model strengthening
Logistic regression is lightweight, but tree-based models are strong on Kaggle.
- XGBoost
- LightGBM
- CatBoost (strong with categorical variables)
Especially CatBoost requires almost no preprocessing and can give strong scores even for beginners.
4. Ensemble
- Averaging multiple models
- Stacking (meta-model)
- VotingClassifier
Even just averaging logistic regression and LightGBM often improves the ranking.
5. Hyperparameter tuning
- GridSearchCV
- RandomizedSearchCV
- Optuna (powerful and reproducible)
LightGBM × Optuna is a classic combo.
6. Introducing cross-validation (CV)
Currently it’s a simple train/test split.
On Kaggle, KFold (5–10) is almost essential.
- Increases score stability
- Helps prevent overfitting
- Reduces the gap with the submission score
7. Use EDA (exploratory data analysis) to grasp the direction
- Correlation heatmap
- Visualize relationships with Heart Disease
- Check feature distributions
This helps you see “which features should be engineered.”
And here is the long-awaited ultimate strategy! 🔥
🌟 My idea of the ultimate strategy (Copilot style)
Here’s the strongest strategy I can think of.
🔥 Copilot-style ultimate strategy
① Do EDA → Form hypotheses about features
(Leave plots and comments in a notebook to create a “research log”)
② Use CatBoost as the base model
- Automatically handles categorical variables
- Requires little preprocessing
- High reproducibility
- Strong in Kaggle tabular competitions
③ Stabilize with KFold CV (5–10)
④ Optimize hyperparameters with Optuna
- Automatically optimizes
- Can keep logs
- High reproducibility
⑤ Ensemble with logistic regression and LightGBM
- Often yields better scores than single models
- Good balance of “transparency × accuracy”
Thank you!! I appreciate it, but I don’t really understand the terms in the ultimate strategy. Could you explain them a bit more simply?
Copilot’s answer
🔥 To explain the “ultimate strategy” in simple terms…
The strategy I mentioned is a “classic flow” that often works well in Kaggle tabular competitions.
But with so many technical terms, it can be hard to grasp, so I’ll explain it using a cooking recipe analogy.
🥣 ① EDA (exploratory data analysis) = Checking the ingredients
Meaning: The work of looking at the data to understand its characteristics.
- Which features seem important?
- Are there outliers?
- Are the distributions skewed?
- How are the correlations?
In cooking terms, it’s like:
“Opening the fridge and checking what ingredients you have.”
🧂 ② CatBoost (model) = “All-purpose cooker that needs no prep”
Meaning: A powerful machine learning model that automatically handles categorical variables.
- Almost no preprocessing needed
- High accuracy
- High reproducibility
- Often wins on Kaggle
In cooking terms, it’s like:
“A high-performance cooker that makes things tasty just by throwing in the ingredients.”
🔁 ③ KFold CV (cross-validation) = Tasting multiple times
Meaning: A method of splitting the data multiple times for training and evaluation to get a stable score.
- A single evaluation can be unstable
- Splitting into 5–10 folds makes it more stable
- Almost essential on Kaggle
In cooking terms, it’s like:
“Tasting not just once, but around five times to adjust the flavor.”
🎛 ④ Optuna (automatic hyperparameter tuning) = Automatic seasoning robot
Meaning: A tool that automatically optimizes the model’s settings (parameters).
- Much faster than tuning by hand
- Leaves reproducible logs
- Often used on Kaggle
In cooking terms, it’s like:
“A robot that automatically optimizes salt, pepper, and heat level.”
🧪 ⑤ Ensemble (combining multiple models) = “Blending flavors”
Meaning: A method of mixing predictions from multiple models to improve accuracy.
- Logistic regression
- LightGBM
- CatBoost
Combining them often makes them stronger than any single model.
In cooking terms, it’s like:
“Using not just soy sauce, but also a bit of mirin and dashi to make it tastier.”
🌟 Summary: Ultimate strategy (easy version)
1 Carefully observe the data (EDA)
2 Use a strong model called CatBoost
3 Taste multiple times (KFold)
4 Use an automatic seasoning robot (Optuna) for optimization
5 Blend multiple models (ensemble)
I see! Then I’m thinking of doing 2, 3, 4, and 5 first, and after that, taking my time with 1 while watching how things go. What do you think?
Copilot’s answer
Nice, that order makes a lot of sense, and honestly, I’d even recommend that order myself.
Then, could you output an all-in-one code that includes 2, 3, 4, and 5? I’ll send the input/output format again.
I re-uploaded 📝 sample_submission.csv and the file with only the top 5 rows cut out from 📝 train.csv.
Copilot’s answer
Got it. I’ll output a fully integrated code that combines 2 (CatBoost), 3 (KFold logistic regression),
4 (Optuna), and 5 (ensemble).
### Below is the Python code ###
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename))
# =========================
# 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()
# Optionally: specify integer columns to treat as categorical
# Example: cat_cols += ['Sex', 'Chest pain type', 'FBS over 120', 'EKG results', 'Exercise angina', 'Slope of ST', 'Number of vessels fluro', 'Thallium']
# For now, we proceed assuming all are numeric.
# =========================
# 2. KFold Logistic Regression
# =========================
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import KFold
from sklearn.metrics import roc_auc_score
kf = KFold(n_splits=5, shuffle=True, random_state=42)
oof_lr = np.zeros(len(train))
test_pred_lr = np.zeros(len(test))
for fold, (trn_idx, val_idx) in enumerate(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]
scaler = StandardScaler()
X_trn_scaled = scaler.fit_transform(X_trn)
X_val_scaled = scaler.transform(X_val)
X_test_scaled = scaler.transform(X_test)
lr = LogisticRegression(max_iter=500)
lr.fit(X_trn_scaled, y_trn)
oof_lr[val_idx] = lr.predict_proba(X_val_scaled)[:, 1]
test_pred_lr += lr.predict_proba(X_test_scaled)[:, 1] / kf.n_splits
print("Logistic Regression CV AUC:", roc_auc_score(y, oof_lr))
# =========================
# 3. CatBoost Model
# =========================
from catboost import CatBoostClassifier, Pool
# CatBoost uses Pool format
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)
# Baseline CatBoost
cb_params = {
'loss_function': 'Logloss',
'eval_metric': 'AUC',
'random_seed': 42,
'verbose': 0,
'depth': 6,
'learning_rate': 0.05,
'iterations': 1000
}
cb_model = CatBoostClassifier(**cb_params)
cb_model.fit(train_pool)
oof_cb = cb_model.predict_proba(train_pool)[:, 1]
test_pred_cb = cb_model.predict_proba(test_pool)[:, 1]
print("CatBoost (baseline) AUC (train):", roc_auc_score(y, oof_cb))
# =========================
# 4. CatBoost tuning with Optuna
# =========================
import 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_cv = Pool(X_trn, label=y_trn, cat_features=cat_cols if len(cat_cols) > 0 else None)
val_pool_cv = Pool(X_val, label=y_val, cat_features=cat_cols if len(cat_cols) > 0 else None)
model = CatBoostClassifier(**params)
model.fit(train_pool_cv, eval_set=val_pool_cv, use_best_model=True)
oof[val_idx] = model.predict_proba(val_pool_cv)[:, 1]
return roc_auc_score(y, oof)
study = optuna.create_study(direction='maximize')
study.optimize(objective, n_trials=20) # Increase/decrease depending on time
print("Best trial:", study.best_trial.params)
best_params = study.best_trial.params
best_params.update({
'loss_function': 'Logloss',
'eval_metric': 'AUC',
'random_seed': 42,
'verbose': 0,
'iterations': 1000
})
cb_best = CatBoostClassifier(**best_params)
cb_best.fit(train_pool)
oof_cb_best = cb_best.predict_proba(train_pool)[:, 1]
test_pred_cb_best = cb_best.predict_proba(test_pool)[:, 1]
print("CatBoost (tuned) AUC (train):", roc_auc_score(y, oof_cb_best))
# =========================
# 5. Ensemble
# =========================
# Here we average:
# - KFold Logistic Regression
# - Tuned CatBoost
test_pred_ensemble = (test_pred_lr + test_pred_cb_best) / 2
# =========================
# 6. Create submission file
# =========================
submission = pd.DataFrame({
'id': test['id'],
'Heart Disease': test_pred_ensemble
})
submission.to_csv('submission.csv', index=False)
print("Created submission.csv.")
No errors on the first run!
However, tuning takes quite a long time, so I want to do something about that.
Computation time: about 1.5 hours.
Here is the completed code
📊 Results (Score & Ranking)
Score: 0.95209 (Previous: 0.94808)
Rank: 1606/2373 (Previous: 1506/1954)
⇒ The score improved, but there’s still a long way to go before reaching the top half.
📚 Insights
1. Copilot can output complex models too
2. As a strategy: first solidify one model, then move to data understanding
3. With rough instructions, computation time becomes long and the results aren’t competitive in the current environment
🔮 Next
Next time, I want to organize the effects and efficiency of model selection more carefully.
English translation by Copilot.
