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
Hunter–prey optimizer (HPO) is a newest 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 degree update method based on the position movement method of the hunter and prey .
This project optimizes the recurrent neural network classification model through the HPO hunter-prey optimization 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 tools
Use the head() method of the Pandas tool to view the first five rows of data:
key code:
3.2 Check missing data
Use the info() method of the Pandas tool to view data information:
As can be seen from the figure above, there are a total of 11 variables, no missing values in the data, and a total of 2000 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 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. 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:
5.3 Data sample dimension increase
The data shape of the data sample after adding dimensions:
6. Build the HPO hunter-prey optimization algorithm to optimize the LSTM classification model
Mainly use the HPO hunter-prey optimization algorithm to optimize the LSTM classification algorithm for object classification.
6.1 HPO hunter-prey optimization algorithm to find optimal parameter values
Optimal parameters:
6.2 Optimal parameter value construction model
6.3 Optimal parameter model summary information
6.4 Optimal parameter model network structure
6.5 Optimal parameter model training set test set loss and accuracy curve
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 0.8512, 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 0.86; the F1 score of classification 1 is 0.85.
7.3 Confusion Matrix
As can be seen from the above figure, there are 21 samples that are actually 0 and predicted to be not 0; 36 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 HPO hunter-prey optimization algorithm to find the optimal parameter values of the recurrent neural network LSTM 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.
def __init__(self, m, T, lb, ub, R, C, X_train, y_train, X_test, y_test):
self.M = m # 种群个数
self.T = T # 迭代次数
self.lb = lb # 下限
self.ub = ub # 上限
self.R = R # 行
self.C = C # 列
self.b = 0.1 # 调节参数
self.X_train = X_train # 训练集特征
self.X_test = X_test # 测试集特征
self.y_train = y_train # 训练集标签
self.y_test = y_test # 测试集标签
# ******************************************************************************
# 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/1-P7LMzRZysEV1WgmQCpp7A
# 提取码：5fv7
# ******************************************************************************
# 提取特征变量和标签变量
y = df['y']
X = df.drop('y', axis=1)
# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
For more project practice, see the list of machine learning project practice collections:
List of actual combat collections of machine learning projects