Boost Your Models with LightGBM: Fast, Accurate Gradient Boosting in Python

Introduction

At the end of 2016, Microsoft’s DMTK team open‑sourced LightGBM on GitHub, quickly gaining over 1,000 stars and 200 forks, demonstrating its popularity.

Gradient Boosting Decision Tree (GBDT) remains a long‑standing model in machine learning, using weak learners (decision trees) iteratively to produce a strong model with good training performance and resistance to over‑fitting. GBDT is widely used in industry for click‑through‑rate prediction, search ranking, and dominates many Kaggle competition solutions.

LightGBM (Light Gradient Boosting Machine) is a lightweight framework implementing the GBDT algorithm, offering high‑efficiency parallel training and several advantages:

Faster training speed

Lower memory consumption

Higher accuracy

Support for parallel learning

Ability to handle large‑scale data

Python Implementation

Classification Model

Training the model

import lightgbm as gbm
from sklearn.datasets import load_iris
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split

# read in the iris data
iris = load_iris()

X = iris.data
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# train the model
model = gbm.LGBMClassifier(max_depth=5, learning_rate=0.1, n_estimators=160, silent=True, objective='multi:softmax')
model.fit(X_train, y_train)

# predict on the test set
ans = model.predict(X_test)

model.score(X_test, y_test)

Plotting feature importance

plt.figure()
gbm.plot_importance(model)
plt.show()

Plotting the tree

plt.figure()
gbm.plot_tree(model)
plt.show()

Regression Model

Training the model

import lightgbm as gbm
from sklearn.datasets import load_iris
from matplotlib import pyplot as plt
from sklearn.model_selection import train_test_split

# read in the iris data
iris = load_iris()

X = iris.data[:, :3]
y = iris.data[:, 3]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

# train the model
model = gbm.LGBMRegressor(max_depth=5, learning_rate=0.1, n_estimators=160, silent=True)
model.fit(X_train, y_train)

# predict on the test set
ans = model.predict(X_test)

model.score(X_test, y_test)

Plotting feature importance

plt.figure()
gbm.plot_importance(model)
plt.show()

Plotting the tree

plt.figure()
gbm.plot_tree(model)
plt.show()

Reference

https://blog.csdn.net/anshuai_aw1/article/details/83659932