Employee's knowledge is an organization asset. Turnover may impose apparent
and hidden costs and irreparable damages. To overcome and mitigate this risk,
employee's condition should be monitored. Due to high complexity of analyzing
well-being features, employee's turnover predicting can be delegated to machine
learning techniques. In this paper, we discuss employee's attrition rate. Three
different supervised learning algorithms comprising AdaBoost, SVM and
RandomForest are used to benchmark employee attrition accuracy. Attained models
can help out at establishing predictive analytics.

员工知识是组织的资产，员工流失可能造成明显和隐藏的成本和不可修复的损害。为了克服和减轻这种风险，应监控员工的状况。由于分析员工福祉特征的复杂性较高，员工离职预测可以委托给机器学习技术。本文讨论了员工的离职率，并使用 AdaBoost、SVM 和 RandomForest 这三种不同的监督学习算法来评估员工离职的准确性。获得的模型可以帮助建立预测分析。

使用机器学习算法进行员工离职率分析

Employee Turnover Analysis Using Machine Learning Algorithms

We present explicit formulas - that are also computer code - for 101
real-life quantitative trading alphas. Their average holding period
approximately ranges 0.6-6.4 days. The average pair-wise correlation of these
alphas is low, 15.9%. The returns are strongly correlated with volatility, but
have no significant dependence on turnover, directly confirming an earlier
result based on a more indirect empirical analysis. We further find empirically
that turnover has poor explanatory power for alpha correlations.

本文提供 101 个实用量化交易 alpha 的具体公式和计算机代码，其平均持有期约为 0.6-6.4 天。这些 alpha 之间的平均相对关联较低，为 15.9%。该研究进一步发现，交易频率对 alpha 相关性的解释能力较差。