Description: 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 Obtain.
1. Project background
Genetic Algorithm (GA) was first proposed by John Holland in the United States in the 1970s. This algorithm is designed and proposed according to the evolution law of organisms in nature. It is a calculation model of the biological evolution process that simulates the natural selection and genetic mechanism of Darwin’s biological evolution theory, and it is a method to search for the optimal solution by simulating the natural evolution process. The algorithm converts the solving process of the problem into a process similar to the crossover and mutation of chromosome genes in biological evolution by means of mathematics and computer simulation operations. When solving more complex combinatorial optimization problems, compared with some conventional optimization algorithms, usually better optimization results can be obtained faster. Genetic algorithm has been widely used in combinatorial optimization, machine learning, signal processing, adaptive control and artificial life and other fields.
This project optimizes the Catboost classification model through GA genetic 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 View missing data
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 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 Create feature data and label data
The key code is as follows:
5.2 Dataset Split
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 GA genetic algorithm to optimize CATBOOST classification model
Mainly use the GA genetic algorithm to optimize the CATBOOST classification algorithm for object classification.
6.1 GA genetic 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 0.8315, indicating that the model has a better effect.
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 0.83.
7.3 Confusion Matrix
As can be seen from the above figure, there are 12 samples that are actually 0 and predicted to be not 0; there are 18 samples that are actually 1 and predicted to be not 1, and the overall prediction accuracy is good.
8. Conclusion and prospect
To sum up, this paper uses the GA genetic algorithm to find the optimal parameter value of the CATBOOST 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.
# Initialize population, initial solution
Sol = np.zeros((N_pop, d)) # initial position
Fitness = np.zeros((N_pop, 1)) # Initialize fitness
for i in range(N_pop): # iteration population
Sol[i] = np.random.uniform(Lower_bound, Upper_bound, (1, d)) # generate random numbers
Fitness[i] = objfun(Sol[i]) # Fitness
# *************************************************** *******************************
# The materials required for the actual combat of this machine learning project, the project resources are as follows:
# project instruction:
# Link: https://pan.baidu.com/s/1c6mQ_1YaDINFEttQymp2UQ
# Extract 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 '-' is displayed as a square in the saved image
data_tmp = df.loc[df['y'] == 1, 'x1'] # filter out samples with y=1
# Draw a 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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