Explanation: This is a machine learning practical project (with data + code + documentation + video explanation). If you need data + code + documentation + video explanation, you can go directly to the end of the article to get it.
1. Project background
The bat algorithm is a heuristic search algorithm proposed by Professor Yang based on swarm intelligence in 2010, and it is an effective method for searching the global optimal solution. The algorithm is based on iterative optimization, initialized to a set of random solutions, and then iteratively searches for the optimal solution, and generates local new solutions by random flight around the optimal solution to enhance the local search speed. The algorithm has the characteristics of simple implementation and few parameters.
In view of the defects of the basic bat algorithm such as slow convergence speed, easy to fall into local optimum, and low solution accuracy, a hybrid bat algorithm combined with local search is proposed to solve unconstrained optimization problems. The algorithm uses chaotic sequences to initialize the position and speed of bats, which lays the foundation for the diversity of global search; integrates Powell search to enhance the local search ability of the algorithm and speed up the convergence speed; uses mutation strategy to avoid the algorithm from falling into local optimum to a certain extent.
This project optimizes the random forest classification model through the HBA hybrid bat intelligence algorithm.
2. Data Acquisition
The modeling data for this time comes from the Internet (compiled by the author of this project), and the statistics of the data items are as follows:
The data details are as follows (partial display):
3. Data preprocessing
3.1 View data with Pandas tool
Use the head() method of the Pandas tool to view the first five rows of data:
key code:
3.2 Data missing view
Use the info() method of the Pandas tool to view data information:
As can be seen from the above figure, there are a total of 9 variables, no missing values in the data, and a total of 1000 data.
key code:
3.3 Data descriptive statistics
Use the describe() method of the Pandas tool to view the mean, standard deviation, minimum, quantile, and maximum of the data.
The key code is as follows:
4. Exploratory Data Analysis
4.1 Histogram of y variables
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. A positive value is a positive correlation, and a negative value is a negative correlation.
5. Feature engineering
5.1 Establish feature data and label data
The key code is as follows:
5.2 Dataset splitting
Use the train_test_split() method to divide according to 80% training set and 20% test set. The key code is as follows:
6. Construct the HBA hybrid bat optimization algorithm to optimize the random forest classification model
Mainly use the HBA hybrid bat optimization algorithm to optimize the random forest classification algorithm for object classification.
6.1 HBA hybrid bat optimization algorithm to find the optimal parameter value
Optimal parameters:
6.2 Optimal parameter value construction model
7. Model Evaluation
7.1 Evaluation indicators and results
Evaluation indicators mainly include accuracy rate, precision rate, recall rate, F1 score and so on.
It can be seen from the above table that the F1 score is 0.9091, indicating that the model works well.
The key code is as follows:
7.2 Classification report
As can be seen from the above figure, the F1 score of classification 0 is 0.93; the F1 score of classification 1 is 0.91.
7.3 Confusion Matrix
As can be seen from the above figure, there are 9 samples that are actually 0 and predicted to be not 0; 7 samples are actually predicted to be 1 and not 1, and the overall prediction accuracy is good.
8. Conclusion and Outlook
To sum up, this paper uses the HBA hybrid bat intelligent optimization algorithm to find the optimal parameter value of the random forest algorithm to build a classification model, and finally proves that the model we proposed works well. This model can be used for forecasting of everyday products.
# 初始化种群、初始解
Sol = np.zeros((N_pop, d)) # 初始化位置
Fitness = np.zeros((N_pop, 1)) # 初始化适用度
for i in range(N_pop): # 迭代种群
Sol[i] = np.random.uniform(Lower_bound, Upper_bound, (1, d)) # 生成随机数
Fitness[i] = objfun(Sol[i]) # 适用度
# ******************************************************************************
# 本次机器学习项目实战所需的资料，项目资源如下：
# 项目说明：
# 链接：https://pan.baidu.com/s/1c6mQ_1YaDINFEttQymp2UQ
# 提取码：thgk
# ******************************************************************************
# y=1样本x1变量分布直方图
fig = plt.figure(figsize=(8, 5)) # 设置画布大小
plt.rcParams['font.sans-serif'] = 'SimHei' # 设置中文显示
plt.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题
data_tmp = df.loc[df['y'] == 1, 'x1'] # 过滤出y=1的样本
# 绘制直方图 bins：控制直方图中的区间个数 auto为自动填充个数 color：指定柱子的填充色
plt.hist(data_tmp, bins='auto', color='g')For more project practice, see the list of machine learning project practice collections:
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