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1. Project background
The Sparrow Search Algorithm (SSA) is a new type of swarm intelligence optimization algorithm proposed in 2020. It is mainly inspired by the foraging behavior and anti-predation behavior of sparrows.
In the process of foraging for sparrows, they are divided into discoverers (explorers) and joiners (followers). Discoverers are responsible for finding food in the population and providing foraging areas and directions for the entire sparrow population, while joiners use Discoverers come to get food. In order to obtain food, sparrows can usually adopt two behavioral strategies: finder and joiner for foraging. Individuals in a population monitor the behavior of other individuals in the population, and attackers in the population compete for food resources with high-intake peers to increase their own predation rate. Additionally, sparrow populations engage in anti-predator behavior when they perceive danger.
This project optimizes the CATBOOST classification model through the SSA intelligent sparrow search algorithm.
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 plot() method of the Matplotlib tool to draw a histogram:
4.2 y=1 sample x1 variable distribution histogram
Use the hist() method of the Matplotlib tool to draw a histogram:
4.3 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 SSA intelligent sparrow search algorithm to optimize CATBOOST classification model
The SSA intelligent sparrow search algorithm is mainly used to optimize the CATBOOST classification algorithm for target classification.
6.1 SSA intelligent sparrow search algorithm finds optimal parameter values
Optimal parameters:
6.2 Model construction with optimal parameter values
7. Model evaluation
7.1 Evaluation indicators and results
The evaluation indicators mainly include accuracy rate, precision rate, recall rate, F1 score, etc.
As can be seen from the table above, the F1 score is 0.9158, indicating that the model is effective.
The key code is as follows:
7.2 Classification Report
As can be seen from the above figure, the F1 score for classification 0 is 0.92; the F1 score for classification 1 is 0.92.
7.3 Confusion Matrix
As can be seen from the above figure, there are 10 samples that are actually 0 and are not predicted to be 0; there are 6 samples that are actually 1 and are not predicted to be 1. The overall prediction accuracy is good.
8. Conclusion and outlook
To sum up, this article uses the SSA intelligent sparrow search algorithm to find the optimal parameter values of the CATBOOST algorithm to build a classification model, which ultimately proves that the model we proposed works well. This model can be used for predictions of everyday products.
# Define boundary function
def Bounds(s, Lb, Ub):
temp=s
for i in range(len(s)):
if temp[i] < Lb[0, i]: # Less than the minimum value
temp[i] = Lb[0, i] # Take the minimum value
elif temp[i] > Ub[0, i]: # Greater than the maximum value
temp[i] = Ub[0, i] # take the maximum value
#************************************************ *******************************
# 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/1c6mQ_1YaDINFEttQymp2UQ
# Extraction code: thgk
#************************************************ *******************************
# y=1 sample x1 variable distribution histogram
fig = plt.figure(figsize=(8, 5)) #Set the canvas size
plt.rcParams['font.sans-serif'] = 'SimHei' # Set Chinese display
plt.rcParams['axes.unicode_minus'] = False # Solve the problem that the negative sign '-' in the saved image is displayed as a square
data_tmp = data.loc[data['y'] == 1, 'x1'] # Filter out samples with y=1
# Draw histogram bins: control the number of intervals in the histogram auto is the number of automatic filling color: specify the filling color of the column
plt.hist(data_tmp, bins='auto', color='g')
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