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本文介绍了金融行业中机器学习的应用，包括算法交易、投资组合管理、保险承保和欺诈检测等。讨论了监督学习和无监督学习，以及它们在回归和分类任务中的应用。文章通过实例展示了如何使用scikit-learn进行机器学习，并探讨了回归模型的风险指标，如MAE、MSE、解释方差得分和R^2。此外，还涉及了分类模型，如逻辑回归、SVM、LDA和QDA，以及评估指标如准确率、精确率和F1分数。
Machine learning is being rapidly adopted for a range of applications in the financial services industry. The adoption of machine learning in financial services has been driven by both supply factors, such as technological advances in data storage, algorithms, and computing infrastructure, and by demand factors, such as profitability needs, competition with other firms, and supervisory and regulatory requirements. Machine learning in finance includes algorithmic trading, portfolio management, insurance underwriting保险承保, and fraud detection, just to name a few subject areas.
There are several types of machine learning algorithms, but the two main ones that you will commonly come across in machine learning literature are supervised and
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