Article Directory
1. Application and test code
In my previous article, I shared how to apply for the internal test of the Spark Cognitive Model, and provided a python code that can be used. For details, see the address:
https://blog.csdn.net/weixin_46211269/article/details/131720896
2. Comparison between measured GPT4.0 and Xinghuo cognitive model
2.1 Test site
In my image, GPT4.0 and Spark Cognitive Model are connected, so I use it for comparison. address:
https://ai.zcai.cloud
https://chat.zcai.cloud/
2.2 Comparison of classic questions
- Are Lu Xun and Zhou Shuren the same person?
GPT4.0 Answer:
Spark Cognitive Model Answer:
- There are ten birds in the tree, and the hunter kills one with a gun, how many birds are there in the tree?
GPT4.0 answer:
The Xinghuo Cognitive Model replied:
2.3 Comparison of code questions
Question: Please help me complete a complete random forest code, including loading data, data processing, model training, loading, visual analysis, etc.
GPT4.0 answer:
# 导入所库
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
from sklearn.datasets import load_iris
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
# 加载数据
iris = load_iris()
X = iris.data
y = iris.target
# 数据处理
X_train, X_test, y_train, y_test = train_test(X y, test_size=0.3, random_state=1)
# 模型训练
clf = RandomForestClassifier(n_estimators=100)
clf.fit(X_train, y)
# 模型评估
y_pred = clf.predict(X_test)
print('Accuracy: ', accuracy_score(y_test, y_pred))
# 特征重要性可视化
feature_imp = pd.Series(clf.feature_importances_,index=iris.feature_names).sort_values(ascending=False)
sns.barplot(x=feature_imp, y=feature_imp.index)
plt.xlabel('Feature Score')
plt.ylabel('Features')
plt.title("Visualizing Important Features")
plt.show()
The Xinghuo Cognitive Model replied:
# 导入所需库
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, cross_val_score
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
# 加载数据
data = pd.read_csv('data.csv')
X = data.iloc[:, :-1].values
y = data.iloc[:, -1].values
# 数据处理(这里假设数据已经处理好，不需要额外处理)
# 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)
# 创建随机森林模型
rf = RandomForestClassifier(n_estimators=100, random_state=42)
# 训练模型
rf.fit(X_train, y_train)
# 预测
y_pred = rf.predict(X_test)
# 评估模型
print("准确率：", accuracy_score(y_test, y_pred))
print("混淆矩阵：", confusion_matrix(y_test, y_pred))
print("分类报告：", classification_report(y_test, y_pred))
# 可视化分析(这里假设数据已经可视化，不需要额外可视化)
2.4 Comparison of thesis questions
Question: I need to complete a thesis titled: Fraud Analysis and Detection Based on Machine Learning, please help me generate a reasonable thesis outline, as detailed as possible to three-level titles
GPT4.0 answer:
一、引言
1.1 研究背景
1.2 研究目的
1.3 研究意义
.4 论文结
二、机器学习概述
2.1 器学习定义## 2.2 机器学习类型
2.2.1 监督学习
2.2.2 无监督学习
2.2.3 半监督学习
2.2.4 强化学习
2.3 机器学习应用领域
三、欺诈分析检测的必要性和挑战
3.1 欺诈行为的严重性
3.2 欺诈检测的挑战
3.2.1 数据问题
3.2.2 特征选择问题
3.2.3 类别不平衡问题
3.3 机器学习在欺诈检测中的作
四、基机器学习的欺诈分析检测方法
4.1 数据预处理
4.1.1 数据清洗
4.1.2 数据转换
4.1.3 特征选择
4.2 机器学习模型构建
4.2.1 选择模型
4.2.2 训练模型
4.2.3 模型评估
4.3 案例分析
五、未来展望和研究方向
5.1 引入新的机器学习技术
5.2 欺诈分析检测模型的改进
5.3 欺诈行为预防
六、结论
6.1 研究成果
6.2 局限性
6.3 对后续研究的建议
The Xinghuo Cognitive Model replied:
I. 引言
A. 研究背景和意义
B. 国内外研究现状
C. 研究内容和方法
II. 机器学习基础
A. 机器学习概述
B. 支持向量机算法原理及应用
C. 决策树算法原理及应用
III. 欺诈行为分析与预测模型的建立
A. 欺诈行为的概念和类型分析
B. 建立欺诈检测预测模型的方法和流程
1. 数据收集和预处理
2. 特征提取和选择
3. 建立分类器模型
4. 对模型进行评估和优化
IV. 实验设计与结果分析
A. 实验设计
1. 数据集介绍
2. 评价指标的选择
3. 实验环境配置
B. 结果分析与讨论
1. 对不同算法的性能比较
2. 对不同特征的选取效果分析
3. 对模型优化的效果分析
V. 结论与展望
A. 主要研究成果总结
B.存在的问题及展望
C.后续工作的建议和展望
VI.参考文献
2.5 Evaluation
After testing in three directions, we can see that there is almost no difference between the two on classic questions; on code questions, there is almost no gap, of which GPT4.
Although I only did some tests, and did not have a large-scale test, judging from the performance of the Spark Cognitive Model, I have already felt the rise of domestic AI. According to gossip, the Xinghuo cognitive model will be iterated again next month, and it may soon reach the effect of 4.0. Of course, we can see from these basic tests that the Spark Cognitive Model has surpassed GPT3.5. My evaluation is that it is based on the gap between GPT3.5 and GPT4.0 and will soon reach the level of GPT4.0.
I am very happy to see the rise of domestic AI, and hope that it will do better in the future.