predictive

简明释义

英[prɪˈdɪktɪv]美[prɪˈdɪktɪv]

adj. 预言性的；成为前兆的

英英释义

单词用法

同义词

反义词

例句

1.The predictive signal is a steady rhythm of alpha waves emanating from the brain's right hemisphere, which are closely associated with relaxing activities.

预测信号是大脑右半球发出的稳定节奏阿尔法波，与放松身心的活动密切相关。

2.So how predictive are big one-day falls of subsequent recession?

那么单日大幅下跌对随之而来的衰退有多高的预见性呢？

3.Envelope 2 Predictive elements.

第二个信封预测性元素。

4.It is a predictive, proactive model of designing software.

这是一个预测模型，设计软件的预测模型。

5.Chetty has not yet issued a comprehensive analysis of the relative predictive power of each of these factors.

切蒂还没有对这些因素中每一个的相对预测能力进行全面分析。

6.It was really pretty exciting for scientists at that time to find these missing elements and confirm their predictive properties.

对当时的科学家来说，找到这些缺失的元素并证实它们被预测的特性，确实是非常令人兴奋的。

7.Predictive accuracy did not differ by gender, race, or education.

预测的精确性不受性别、种族或受教育程度影响。

8.Ellenbogen says this gives researchers a new predictive power.

Ellenbogen说，这给研究人员新的研究动力。

9.The company uses predictive analytics to forecast sales for the upcoming quarter.

公司使用预测性的分析来预测下个季度的销售情况。

10.The weather service employs predictive algorithms to improve the accuracy of their forecasts.

天气服务部门采用预测性算法来提高其预测的准确性。

11.The software includes predictive features that suggest actions based on user behavior.

该软件包含预测性功能，根据用户行为建议操作。

12.Retailers rely on predictive analytics to optimize inventory and reduce stockouts.

零售商依靠预测性分析来优化库存并减少缺货。

13.In healthcare, predictive modeling helps identify patients at risk of developing chronic diseases.

在医疗保健中，预测性建模有助于识别有发展慢性病风险的患者。

作文

In today's rapidly evolving world, the ability to make accurate predictions is more important than ever. This is where the concept of predictive (预测的) analytics comes into play. By leveraging data and statistical algorithms, organizations can foresee potential outcomes and trends, allowing them to make informed decisions that can significantly impact their success. One of the most prominent applications of predictive (预测的) analytics is in the field of marketing. Companies analyze consumer behavior patterns to anticipate what products or services will be in demand. For instance, retailers often use predictive (预测的) models to determine which items are likely to sell during a particular season. This not only helps in managing inventory effectively but also enhances customer satisfaction by ensuring that popular products are readily available. In healthcare, predictive (预测的) analytics has transformed patient care. By examining historical data, medical professionals can identify patients who are at risk of developing certain conditions. This proactive approach enables healthcare providers to implement preventive measures, ultimately improving patient outcomes. For example, hospitals can use predictive (预测的) models to forecast patient admissions, allowing them to allocate resources more efficiently and reduce wait times. Moreover, the finance sector heavily relies on predictive (预测的) analytics for risk assessment and fraud detection. Financial institutions utilize algorithms to analyze transaction patterns and flag any unusual activities that may indicate fraudulent behavior. By being able to predict potential risks, banks can take necessary precautions to protect their assets and maintain customer trust. Education is another area where predictive (预测的) analytics is making strides. By analyzing student performance data, educators can identify students who may be struggling and provide targeted interventions. This predictive (预测的) approach allows for personalized learning experiences that cater to individual needs, ultimately leading to better academic outcomes. Despite its numerous advantages, the use of predictive (预测的) analytics does come with challenges. One major concern is the ethical implications of data usage. Organizations must ensure that they are collecting and using data responsibly, respecting privacy and consent. Additionally, reliance on predictive (预测的) models can sometimes lead to overconfidence in outcomes, causing organizations to overlook other important factors that may influence results. In conclusion, the power of predictive (预测的) analytics cannot be underestimated. As technology continues to advance, the ability to analyze data and make accurate predictions will become increasingly vital across various industries. Embracing this capability can lead to improved decision-making, enhanced efficiency, and ultimately, greater success. However, it is essential for organizations to approach predictive (预测的) analytics with caution, ensuring ethical practices are upheld while harnessing the potential of data-driven insights.

在当今快速发展的世界中，准确预测的能力比以往任何时候都更加重要。这就是predictive（预测的）分析概念发挥作用的地方。通过利用数据和统计算法，组织可以预见潜在的结果和趋势，从而做出明智的决策，这对他们的成功产生重大影响。predictive（预测的）分析的一个显著应用是在营销领域。公司分析消费者行为模式，以预测哪些产品或服务将受到需求。例如，零售商常常使用predictive（预测的）模型来确定在特定季节可能畅销的商品。这不仅有助于有效管理库存，还通过确保热门产品随时可用来增强客户满意度。在医疗保健方面，predictive（预测的）分析已经改变了患者护理。通过检查历史数据，医疗专业人员可以识别出有患某些疾病风险的患者。这种主动的方法使医疗提供者能够实施预防措施，从而改善患者的结果。例如，医院可以使用predictive（预测的）模型来预测患者入院情况，从而更有效地分配资源并减少等待时间。此外，金融行业在风险评估和欺诈检测方面严重依赖predictive（预测的）分析。金融机构利用算法分析交易模式，并标记任何异常活动，这可能表明欺诈行为。通过能够预测潜在风险，银行可以采取必要的预防措施来保护其资产并维护客户信任。教育也是predictive（预测的）分析取得进展的另一个领域。通过分析学生表现数据，教育工作者可以识别出可能面临困难的学生，并提供有针对性的干预。这种predictive（预测的）方法允许个性化的学习体验，以满足个人需求，最终导致更好的学业成果。尽管有许多优点，但使用predictive（预测的）分析也面临挑战。一个主要问题是数据使用的伦理影响。组织必须确保以负责任的方式收集和使用数据，尊重隐私和同意。此外，过度依赖predictive（预测的）模型有时会导致对结果的过度自信，导致组织忽视可能影响结果的其他重要因素。总之，predictive（预测的）分析的力量不可低估。随着技术的不断进步，分析数据和做出准确预测的能力在各个行业中将变得越来越重要。拥抱这一能力可以改善决策，提高效率，最终实现更大的成功。然而，组织在利用数据驱动的洞察力时，必须谨慎对待predictive（预测的）分析，确保遵循伦理实践。