Lightgbm Light Gradient Boosting Machine

Directory

foreword

1. What is Lightgbm?

2. Advantages and disadvantages of Lightgbm

1. Advantages:

2. Disadvantages:

3. Application scenarios of Lightgbm

4. Precautions for building the Lightgbm model

5. Implementation class library of Lightgbm model

6. Evaluation index of Lightgbm model

1. Evaluation indicators for regression tasks:

2. Evaluation indicators for binary classification tasks:

3. Evaluation indicators for multi-classification tasks:

Seven, the class library scikit-learn realizes the example of Lightgbm

1. Return to mission

2. Binary classification task

3. Multiple classification tasks

Summarize

Foreword

Lightgbm is an algorithm for supervised learning in machine learning that can solve regression and classification tasks.

1. What is Lightgbm?

LightGBM is an efficient gradient boosting decision tree algorithm developed by Microsoft Research Asia. It uses a histogram-based decision tree algorithm and a leaf-growing strategy with a depth limit, which reduces memory consumption and computational complexity during training. Compared with the traditional gradient boosting decision tree algorithm, LightGBM has faster training speed and better accuracy, and supports parallel training and multi-classification tasks. In the field of machine learning, LightGBM has become a very popular algorithm.

2. Advantages and disadvantages of Lightgbm

1. Advantages:

  • Efficiency: It adopts a histogram-based decision tree algorithm and a leaf growth strategy with a depth limit, which reduces memory consumption and computational complexity during the training process, resulting in faster training speed and better accuracy.
  • Scalability: Supports parallel training and multi-classification tasks, and can handle large-scale data sets.
  • Accuracy: On some data sets, LightGBM has better accuracy and generalization performance than traditional gradient boosting decision tree algorithms.

2. Cons:

  • Sensitive to noise: Due to the use of smaller leaf nodes, LightGBM is more sensitive to noise, which may lead to overfitting.
  • Difficult parameter adjustment: LightGBM has many parameters that need to be adjusted, and it takes a certain amount of time and effort to adjust the parameters.
  • Does not support online learning: LightGBM does not support online learning and needs to retrain the model to adapt to new data.

LightGBM, like XGBoost, is an optimized and efficient implementation of GBDT. There are some similarities in principle, but it has better performance than XGBoost in many aspects. The official advantages of this tool library model are as follows:

  1. Faster Training Efficiency
  2. low memory usage
  3. higher accuracy
  4. Supports parallel learning
  5. Can handle large-scale data
  6. Support for direct use of the category feature

3. Application scenarios of Lightgbm

Applicable to many application scenarios, including but not limited to the following aspects:

  1. Recommendation system: LightGBM can be used for product recommendation, advertisement recommendation and other tasks in the recommendation system.
  2. Search engine: LightGBM can be used for tasks such as webpage ranking and advertisement ranking in search engines.
  3. Financial risk control: LightGBM can be used for tasks such as credit scoring and fraud detection.
  4. Healthcare: LightGBM can be used for disease diagnosis, drug development and other tasks.
  5. Natural language processing: LightGBM can be used for tasks such as sentiment analysis and text classification.
  6. Image recognition: LightGBM can be used for image classification, object detection and other tasks.
  7. Time series forecasting: LightGBM can be used for stock price forecasting, traffic flow forecasting and other tasks.
  8. Text generation: LightGBM can be used for text generation, machine translation and other tasks.
  9. Reinforcement Learning: LightGBM can be used for tasks such as value function estimation in reinforcement learning.

4. Notes on building the Lightgbm model

  1. Data preprocessing: Perform preprocessing operations such as missing value filling, outlier processing, and standardization on the data to improve the accuracy and generalization performance of the model.
  2. Feature selection: Select features that have strong predictive power for the target variable and avoid using redundant or irrelevant features to improve the accuracy and generalization performance of the model.
  3. Parameter adjustment: LightGBM has many parameters that need to be adjusted, which need to be adjusted according to the actual situation to achieve the best model effect.
  4. Cross-validation: Use cross-validation to evaluate the performance of the model and avoid overfitting or underfitting.
  5. Early stopping: Use early stopping to prevent overfitting and improve the generalization performance of the model.
  6. Model Fusion: Use model fusion techniques to improve model accuracy and generalization performance.
  7. Parallel training: Use parallel training to speed up model training and improve efficiency.
  8. Multi-classification problem processing: For multi-classification problems, appropriate processing is required, such as using one-hot encoding and other methods.
  9. Prevent overfitting: use regularization technology, reduce learning rate and other methods to prevent overfitting and improve the generalization performance of the model.
  10. Model interpretation: explain the model results, analyze the importance of features, influencing factors, etc., to facilitate business decisions.

5. Implementation class library of Lightgbm model

LightGBM can be implemented using a variety of programming languages and machine learning libraries. The following are some commonly used libraries and methods:

  1. Python class libraries: Lightgbm, scikit-learn, xgboost, etc.
  2. R libraries: Lightgbm, xgboost, caret, etc.
  3. Java class library: H2O, xgboost4j, etc.
  4. C++ class library: LightGBM, xgboost, etc.

These class libraries all provide the API interface of LightGBM, which can facilitate model training, parameter adjustment, prediction and other operations. In addition, LightGBM also provides command-line tools and RESTful API, which can facilitate model deployment and service.

Among them, three common class libraries of Python:

  1. Lightgbm class library: This is the Python class library officially provided by LightGBM. It provides a complete LightGBM algorithm implementation and API interface, and supports multiple feature types, parallel training, and multi-classification tasks. In addition, it provides many practical functions, such as feature importance analysis, model interpretation, etc. Model training, prediction and deployment can be conveniently performed using the Lightgbm class library.
  2. The scikit-learn library: scikit-learn is a widely used Python machine learning library that provides implementations of many machine learning algorithms, including LightGBM. The LightGBM class in scikit-learn supports functions such as multi-classification tasks and cross-validation, and is compatible with other machine learning algorithms in scikit-learn, which can facilitate model fusion and comparison.
  3. XGBoost class library: XGBoost is another popular gradient boosting decision tree algorithm that can also be used to implement the LightGBM algorithm. XGBoost provides Python interface and sklearn interface, supports multiple classification tasks, parallel training, feature importance analysis and other functions. Since the XGBoost and LightGBM algorithms have many similarities, they are similar in use.

6. Evaluation indicators of Lightgbm model

The LightGBM model can solve regression and classification tasks, and its corresponding evaluation indicators include:

1. Evaluation index of regression task:

  • Mean Absolute Error (MAE): The average of the absolute values of the difference between the predicted value and the true value.
  • Mean squared error (MSE): The average of the squares of the difference between the predicted value and the true value.
  • Root Mean Square Error (RMSE): The square root of the MSE.
  • R2 score: The square of the correlation coefficient between the predicted value and the true value.

2. Evaluation index of binary classification task:

  • Accuracy: The ratio of the number of correctly predicted samples to the total number of samples.
  • Precision: The ratio of the number of samples predicted to be positive and correct to the number of samples predicted to be positive.
  • Recall rate (Recall): The proportion of the number of samples that are predicted to be positive and correct to the number of samples that are true positive samples.
  • F1 score: The harmonic mean of precision and recall.

3. Evaluation indicators for multi-classification tasks:

  • Accuracy: The ratio of the number of correctly predicted samples to the total number of samples.
  • Log loss function (Log loss): Used to measure the uncertainty of the classifier, the smaller the better.
  • Multi-class Log loss: Calculates the log loss function for each class and then averages it.
  • Confusion matrix (Confusion matrix): used to describe the performance of the classifier, including true positives, false positives, true negatives, false negatives and other indicators.

In short, the evaluation indicators of the LightGBM model vary according to different task types, and users can choose appropriate evaluation indicators for model evaluation and selection according to their own needs.

7. An example of implementing Lightgbm in class library scikit-learn

Here are a few examples of modeling using the Lightgbm library in Python:

1. Return task

import Lightgbm as lgb
from sklearn.datasets import load_boston
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error

# load the dataset
boston = load_boston()
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=42)

# Create the LightGBM dataset
train_data = lgb.Dataset(X_train, label=y_train)
test_data = lgb.Dataset(X_test, label=y_test)

# Set model parameters
params = {
    'boosting_type': 'gbdt',
    'objective': 'regression',
    'metric': 'rmse',
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9,
}

# train the model
model = lgb.train(params, train_data, num_boost_round=1000, valid_sets=[test_data], early_stopping_rounds=100)

# forecast result
y_pred = model. predict(X_test)

# Compute root mean square error
mse = mean_squared_error(y_test, y_pred)
rmse = mse ** 0.5
print('RMSE:', rmse)

2. Binary classification task

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 the dataset
cancer = load_breast_cancer()
X_train, X_test, y_train, y_test = train_test_split(cancer.data, cancer.target, test_size=0.2, random_state=42)

# Create the LightGBM dataset
train_data = lgb.Dataset(X_train, label=y_train)
test_data = lgb.Dataset(X_test, label=y_test)

# Set model parameters
params = {
    'boosting_type': 'gbdt',
    'objective': 'binary',
    'metric': 'binary_logloss',
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9,
}

# train the model
model = lgb.train(params, train_data, num_boost_round=1000, valid_sets=[test_data], early_stopping_rounds=100)

# forecast result
y_pred = model. predict(X_test)
y_pred_binary = [1 if i >= 0.5 else 0 for i in y_pred]

# calculate accuracy
acc = accuracy_score(y_test, y_pred_binary)
print('Accuracy:', acc)

3. Multi-classification task

import Lightgbm as lgb
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# load the dataset
iris = load_iris()
X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2, random_state=42)

# Create the LightGBM dataset
train_data = lgb.Dataset(X_train, label=y_train)
test_data = lgb.Dataset(X_test, label=y_test)

# Set model parameters
params = {
    'boosting_type': 'gbdt',
    'objective': 'multiclass',
    'metric': 'multi_logloss',
    'num_class': 3,
    'num_leaves': 31,
    'learning_rate': 0.05,
    'feature_fraction': 0.9,
}

# train the model
model = lgb.train(params, train_data, num_boost_round=1000, valid_sets=[test_data], early_stopping_rounds=100)

# forecast result
y_pred = model. predict(X_test)
y_pred_class = [list(i).index(max(i)) for i in y_pred]

# calculate accuracy
acc = accuracy_score(y_test, y_pred_class)
print('Accuracy:', acc)

Summary

This article briefly introduces the basic concepts, advantages and disadvantages of Lightgbm, precautions in the modeling process, commonly used class libraries, and modeling code examples.

The knowledge points of the article match the official knowledge files, and you can further learn relevant knowledge