🪔
🎉 Festival Dhamaka Sale – Upto 80% Off on All Courses 🎊
🎁
INDIA'S NO. 1 INTERNSHIP PORTAL
🎉 Festival Dhamaka Sale – Upto 80% Off on All Courses 🎊
🎁INDIA'S NO. 1 INTERNSHIP PORTAL
Speeds up training by discretizing continuous features into bins, reducing memory usage.
Leaf-wise tree growth with depth constraints leads to better accuracy than level-wise methods.
Supports GPU training for faster model building on large datasets.
Built-in support for parallel and distributed learning across multiple machines.
Use pip, conda, or build from source with CMake for full GPU and distributed support.
Use Pandas or NumPy arrays, or convert to LightGBM’s Dataset format for efficiency.
Use `LGBMClassifier` or `LGBMRegressor` with custom parameters for training.
Use built-in metrics and early stopping to monitor performance and avoid overfitting.
Export models and use SHAP or feature importance for explainability in production.
import lightgbm as lgb
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load data
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
# Train model
model = lgb.LGBMClassifier()
model.fit(X_train, y_train)
# Predict and evaluate
preds = model.predict(X_test)
acc = accuracy_score(y_test, preds)
print("Accuracy:", acc)Used for fraud detection, churn prediction, and medical diagnosis with high accuracy.
Predict prices, demand, or risk scores with fast training and low memory usage.
Supports LambdaRank and other ranking objectives for search and recommendation systems.
Handles millions of samples and features efficiently with distributed training.
Explore LightGBM’s ecosystem and find the tools, platforms, and docs to accelerate your workflow.
Common questions about LightGBM’s capabilities, usage, and ecosystem.