Feature Importance Example - Lightgbm Typing CST Test
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Feature Importance Example — Lightgbm Code
Display feature importance after training a model.
import lightgbm as lgb
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
data = load_iris()
X_train, X_test, y_train, y_test = train_test_split(data.data, data.target, test_size=0.2, random_state=42)
train_data = lgb.Dataset(X_train, label=y_train)
params = {'objective':'multiclass','num_class':3,'metric':'multi_logloss'}
model = lgb.train(params, train_data, num_boost_round=100)
lgb.plot_importance(model)
plt.show()Lightgbm Language Guide
LightGBM (Light Gradient Boosting Machine) is a fast, distributed, high-performance gradient boosting framework based on decision tree algorithms, used for ranking, classification, and many other machine learning tasks.
Primary Use Cases
- ▸Binary and multiclass classification
- ▸Regression problems
- ▸Ranking tasks (learning-to-rank)
- ▸Feature selection and importance analysis
- ▸Integration in ML pipelines for large-scale structured data
Notable Features
- ▸Faster training with histogram-based decision tree algorithm
- ▸Low memory usage compared to XGBoost
- ▸Supports parallel and GPU learning
- ▸Handles categorical features directly
- ▸Scales efficiently with large datasets
Origin & Creator
LightGBM was developed by Microsoft’s DMTK team and released in 2016 to provide a faster and more memory-efficient gradient boosting framework compared to existing solutions.
Industrial Note
LightGBM is widely used in Kaggle competitions, finance, advertising, recommendation systems, and any scenario requiring high-speed gradient boosting on large datasets.