## 467

participants

#### Introduction

Employees are the most important asset of a company. For organizations to make steady growth, it is important to detect early signs of employee turnover and retain top talents. The prediction of employee turnover is based on big data and artificial intelligence to analyze possibilities of resignation. Talent retention initiatives can be activated as soon as possible when warnings are identified.

Many indices of employee turnover are collected, such as age, performance records, highest academic degree, number of business trips and number of leaves, and etc. HR professionals need to evaluate the tendency of employees leaving by referring to past experiences and working conditions. In this topic, participants are asked to build models to predict whether the employee will resign by adopting the method of machine learning.

#### Prize

The following awards will be given to the top three participants on the Private Leaderboard whose score is higher than the baseline 0.18 and the report is submitted before 11/4.

First place: 150,000 discount points of hicloud + NTD 15,000 (Tax included)

Second place: 100,000 discount points of hicloud + NTD 10,000 (Tax included)

Third place: 50,000 discount points of hicloud + NTD 5,000 (Tax included)

Honorable mention: 50,000 discount points of hicloud (multiple winners, depends on the final result)

* Hicloud discount points were provided by Chunghwa Telecom

Notes:
1. Hicloud points can redeem service charge. After the discount, the price will be charged at 30% off based on the list price automatically.

2. Chunghwa Telecom reserves the right to make changes to the terms and conditions herein.

3. The example of discount points calculation can be referred to https://aidea-web.tw/computing

#### Activity time

The activity time will be based on Taiwan Standard Time (UTC+8), and the schedule is as follows.

TimeEvent
2020/08/05Registration begins
2020/11/11Awards announcement

#### Evaluation Criteria

Evaluations are conducted by calculating the Mean Absolute Error F beta score [1]，beta = 1.5

The formula is as follows:

$$F_{\beta} = {(1+\beta^2)\cdot {precision \cdot recall \over (\beta ^2 \cdot precision)+recall}} \\$$

Reference
[1] F1 score: https://en.wikipedia.org/wiki/F1_score

#### Rules

• The evaluation will be based on the final uploaded result. If multiple participants get the same evaluation scores, the time of uploading will determine the ranking.
• The maximum number of uploading times is 3 times per day.
• This issue does not offer a team-up option, only one account per person is allowed and each person can only participate once. If any violations are found, people who are involved would be forced to withdraw the activity.
When using external data sets, participants should avoid using future data as the basis of the prediction results, and must state source of data sets in the forum for references.
• All the data, techniques and source codes are original work of participants or are used by permission complying with laws and regulations. If any third-party claims their intellectual property rights or other rights and interests are being violated, the participant will need to handle the disputes personally. If any participants violate intellectual property rights, they will be disqualified, and shall bear legal responsibilities.
• All the achievements and their IPRs (intellectual property rights) belong to the participants, and the Copyright License Agreements, patent applications, technology transfer and equity distributions of them, should be in accordance with the relevant Laws and Regulations.
• There should be no answer discussions between different accounts, or it will be considered as cheating.
• If there is cheating or fraud during the activity, the team that cheats will be disqualified from the activity and the vacancy would be filled up by other teams in the ranking order.
• After uploading, the answers of test data would be divided into two parts to calculate the score:
• Before the deadline of the activity: The system refers to only a part of the test data to examine and calculate the score. The result will be posted on the Public Leaderboard as reference for the final score and ranking. This data accounts for 40% of the entire test data.
• After the deadline of the activity: The system refers to the remaining test data (60%) to examine and calculate the score. The result will be posted on the Private Leaderboard as reference for the final score and ranking.
• After being notified, finalists need to submit their report before 11/4 to be eligible for the award (the report will not be disclosed or published).
• Should there be disputes, the organizer reserves the right to make the final decision.