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1. Project background
Hunter-prey optimizer (HPO) is a latest optimization search algorithm proposed by Naruei & Keynia in 2022. Inspired by the behavior of predators (such as lions, leopards and wolves) and prey (such as stags and gazelles), they designed a new search method and adaptive update method based on the location movement of hunters and prey. .
This project uses the HPO hunter prey optimization algorithm to find optimal parameter values to optimize the LightGBM regression model.
2. Data acquisition
The modeling data for this time comes from the Internet (compiled by the author of this project). The statistics of the data items are as follows:
The data details are as follows (partially displayed):
3. Data preprocessing
3.1 Use Pandas tool to view data
Use the head() method of the Pandas tool to view the first five rows of data:
Key code:
3.2 Missing data view
Use the info() method of the Pandas tool to view data information:
As you can see from the picture above, there are a total of 11 variables, no missing values in the data, and a total of 1,000 pieces of data.
Key code:
3.3 Data descriptive statistics
Use the describe() method of the Pandas tool to view the mean, standard deviation, minimum value, quantile, and maximum value of the data.
The key code is as follows:
4. Exploratory data analysis
4.1 y variable histogram
Use the hist() method of the Matplotlib tool to draw a histogram:
As you can see from the picture above, the y variable is mainly concentrated between -400 and 400.
4.2 Correlation Analysis
As can be seen from the figure above, the larger the value, the stronger the correlation. Positive values are positive correlations, and negative values are negative correlations.
5. Feature Engineering
5.1 Create feature data and label data
The key code is as follows:
5.2 Data set split
The train_test_split() method is used to divide 80% of the training set and 20% of the test set. The key code is as follows:
6. Construct HPO hunter prey optimization algorithm to optimize LightGBM regression model
The HPO hunter prey optimization algorithm is mainly used to optimize the LightGBM regression algorithm for target regression.
6.1 The optimal parameters found by the HPO hunter-prey optimization algorithm
Optimal parameters:
6.2 Optimal parameter values to build the model
7. Model evaluation
7.1 Evaluation indicators and results
Evaluation indicators mainly include explainable variance value, mean absolute error, mean square error, R-squared value, etc.
As can be seen from the above table, the R square is 0.909, which means the model has good effect.
The key code is as follows:
7.2 Comparison chart between true value and predicted value
It can be seen from the above figure that the fluctuations of the real value and the predicted value are basically consistent, and the model fitting effect is good.
8. Conclusion and outlook
To sum up, this paper uses the HPO hunter-prey optimization algorithm to find the optimal parameter values of the LightGBM regression algorithm to build a regression model, which ultimately proves that the model we proposed works well. This model can be used for predictions of everyday products.
def __init__(self, m, T, lb, ub, R, C, X_train, y_train, X_test, y_test):
self.M = m # Number of populations
self.T = T #Number of iterations
self.lb = lb # lower limit
self.ub = ub # upper limit
self.R = R # row
self.C = C # column
self.b = 0.1 #Adjustment parameters
self.X_train = X_train #Training set features
self.X_test = X_test #Test set features
self.y_train = y_train #Training set label
self.y_test = y_test #Test set label
#************************************************ *******************************
# The materials required for the actual implementation of this machine learning project, the project resources are as follows:
# project instruction:
# Link: https://pan.baidu.com/s/1-P7LMzRZysEV1WgmQCpp7A
# Extraction code: 5fv7
#************************************************ *******************************
#Extract feature variables and label variables
y = df['y']
X = df.drop('y', axis=1)
# Divide training set and test set
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
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