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About the test:

就业前机器学习评估测试评估了候选人对机器学习基础的理解,例如功能工程,回归,差异,有条件的概率,聚类,决策树,最近的邻居,幼稚的贝叶斯,偏见,偏见和过度拟合。该测试还评估了他们收集和准备数据集,训练模型,评估模型并迭代改善模型性能的能力。

Covered skills:

  • 线性回归
  • 过度拟合和不足
  • 偏见和差异
  • 监督学习
  • 聚类
  • 模型评估
  • 梯度下降
  • 支持向量机
  • 交叉验证
  • 无监督的学习
  • 减少维度
  • 功能工程

9 reasons why
9 reasons why

Adaface Machine Learning Test is the most accurate way to shortlist 机器学习开发人员s



Reason #1

Tests for on-the-job skills

The Machine Learning Assessment Test helps recruiters and hiring managers identify qualified candidates from a pool of resumes, and helps in taking objective hiring decisions. It reduces the administrative overhead of interviewing too many candidates and saves time by filtering out unqualified candidates at the first step of the hiring process.

The test screens for the following skills that hiring managers look for in candidates:

  • 能够实现和理解线性回归算法
  • 精通梯度下降优化算法
  • 熟悉机器学习模型中过度拟合和不足的概念
  • 能够将支持向量机应用用于分类任务
  • 能够识别和管理机器学习模型中的偏见和差异
  • 熟练的交叉验证技术用于模型评估
  • 经验丰富的学习算法
  • 知识渊博的学习算法
  • 有能力执行聚类任务
  • 能够应用降维技术
  • 熟练评估机器学习模型
  • 擅长执行功能工程以改善模型性能
Reason #2

No trick questions

no trick questions

Traditional assessment tools use trick questions and puzzles for the screening, which creates a lot of frustration among candidates about having to go through irrelevant screening assessments.

View sample questions

The main reason we started Adaface is that traditional pre-employment assessment platforms are not a fair way for companies to evaluate candidates. At Adaface, our mission is to help companies find great candidates by assessing on-the-job skills required for a role.

Why we started Adaface
Reason #3

Non-googleable questions

We have a very high focus on the quality of questions that test for on-the-job skills. Every question is non-googleable and we have a very high bar for the level of subject matter experts we onboard to create these questions. We have crawlers to check if any of the questions are leaked online. If/ when a question gets leaked, we get an alert. We change the question for you & let you know.

How we design questions

这些只是我们库中有10,000多个问题的一个小样本。关于此的实际问题 机器学习评估测试 将是不可行的.

🧐 Question

Easy

Gradient descent optimization
Gradient Descent
Solve
You are working on a regression problem using a simple neural network. You want to optimize the model's weights using gradient descent with different learning rate schedules. Consider the following pseudo code for training the neural network:
 image
Which of the following learning rate schedules would most likely result in the fastest convergence without overshooting the optimal weights?

A: Constant learning rate of 0.01
B: Exponential decay with initial learning rate of 0.1 and decay rate of 0.99
C: Exponential decay with initial learning rate of 0.01 and decay rate of 0.99
D: Step decay with initial learning rate of 0.1 and decay rate of 0.5 every 100 epochs
E: Step decay with initial learning rate of 0.01 and decay rate of 0.5 every 100 epochs
F: Constant learning rate of 0.1

Medium

Less complex decision tree model
Model Complexity
Overfitting
Solve
You are given a dataset to solve a classification problem using a decision tree algorithm. You are concerned about overfitting and decide to implement pruning to control the model's complexity. Consider the following pseudo code for creating the decision tree model:
 image
Which of the following combinations of parameters would result in a less complex decision tree model, reducing the risk of overfitting?

A: max_depth=5, min_samples_split=2, min_samples_leaf=1
B: max_depth=None, min_samples_split=5, min_samples_leaf=5
C: max_depth=3, min_samples_split=2, min_samples_leaf=1
D: max_depth=None, min_samples_split=2, min_samples_leaf=1
E: max_depth=3, min_samples_split=10, min_samples_leaf=10
F; max_depth=5, min_samples_split=5, min_samples_leaf=5

Easy

n-gram generator
Solve
Our newest machine learning developer want to write a function to calculate the n-gram of any text. An N-gram means a sequence of N words. So for example, "black cats" is a 2-gram, "saw black cats" is a 3-gram etc. The 2-gram of the sentence "the big bad wolf fell down" would be [["the", "big"], ["big", "bad"], ["bad", "wolf"], ["wolf", "fell"], ["fell", "down"]]. Can you help them select the correct function for the same?
 image

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
Solve
You are tasked with building a recommendation system for a newly launched e-commerce website. Given that the website is new, there is not much user interaction data available. Also, the items in the catalog have rich descriptions. Based on these requirements, which type of recommendation system approach would be the most suitable for this task?

Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation
Solve
You have trained a supervised learning model to classify customer reviews as either "positive" or "negative" based on a dataset with 10,000 samples and 35 features, including the review text, reviewer's name, and rating. The dataset is split into a 7,000-sample training set and a 3,000-sample test set.

After training the model, you evaluate its performance using a confusion matrix on the test set, which shows the following results:
 image
Based on the confusion matrix, what are the sensitivity and specificity of the model?
🧐 Question🔧 Skill

Easy

Gradient descent optimization
Gradient Descent

2 mins

Machine Learning
Solve

Medium

Less complex decision tree model
Model Complexity
Overfitting

2 mins

Machine Learning
Solve

Easy

n-gram generator

2 mins

Machine Learning
Solve

Easy

Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering

2 mins

Machine Learning
Solve

Easy

Sensitivity and Specificity
Confusion Matrix
Model Evaluation

2 mins

Machine Learning
Solve
🧐 Question🔧 Skill💪 Difficulty⌛ Time
Gradient descent optimization
Gradient Descent
Machine Learning
Easy2 mins
Solve
Less complex decision tree model
Model Complexity
Overfitting
Machine Learning
Medium2 mins
Solve
n-gram generator
Machine Learning
Easy2 mins
Solve
Recommendation System Selection
Recommender Systems
Collaborative Filtering
Content-Based Filtering
Machine Learning
Easy2 mins
Solve
Sensitivity and Specificity
Confusion Matrix
Model Evaluation
Machine Learning
Easy2 mins
Solve
Reason #4

1200+ customers in 75 countries

customers in 75 countries
Brandon

借助 Adaface,我们能够将初步筛选流程优化高达 75% 以上,为招聘经理和我们的人才招聘团队节省了宝贵的时间!


Brandon Lee, 人事主管, Love, Bonito

Reason #5

Designed for elimination, not selection

The most important thing while implementing the pre-employment 机器学习评估测试 in your hiring process is that it is an elimination tool, not a selection tool. In other words: you want to use the test to eliminate the candidates who do poorly on the test, not to select the candidates who come out at the top. While they are super valuable, pre-employment tests do not paint the entire picture of a candidate’s abilities, knowledge, and motivations. Multiple easy questions are more predictive of a candidate's ability than fewer hard questions. Harder questions are often "trick" based questions, which do not provide any meaningful signal about the candidate's skillset.

Science behind Adaface tests
Reason #6

1 click candidate invites

Email invites: You can send candidates an email invite to the 机器学习评估测试 from your dashboard by entering their email address.

Public link: You can create a public link for each test that you can share with candidates.

API or integrations: You can invite candidates directly from your ATS by using our pre-built integrations with popular ATS systems or building a custom integration with your in-house ATS.

invite candidates
Reason #7

Detailed scorecards & benchmarks

Reason #7

Detailed scorecards & benchmarks

Along with scorecards that report the performance of the candidate in detail, you also receive a comparative analysis against the company average and industry standards.

View sample scorecard
Reason #8

High completion rate

Adaface tests are conversational, low-stress, and take just 25-40 mins to complete.

This is why Adaface has the highest test-completion rate (86%), which is more than 2x better than traditional assessments.

test completion rate
Reason #9

Advanced Proctoring


Learn more

About the Machine Learning Online Test

Why you should use Pre-employment Machine Learning Assessment Test?

The 机器学习评估测试 makes use of scenario-based questions to test for on-the-job skills as opposed to theoretical knowledge, ensuring that candidates who do well on this screening test have the relavant skills. The questions are designed to covered following on-the-job aspects:

  • 实施线性回归模型进行预测分析
  • 应用梯度下降算法进行模型优化
  • 识别和缓解机器学习模型中的过度拟合和不足的问题
  • 利用支持向量机进行分类任务
  • 了解机器学习模型中偏见和差异的概念
  • 进行交叉验证以评估监督学习模型的性能
  • 在无监督的学习中应用各种技术,例如聚类
  • 实施降低降低方法以提高模型效率
  • 使用适当的评估指标评估机器学习模型
  • 利用功能工程技术来增强模型性能

Once the test is sent to a candidate, the candidate receives a link in email to take the test. For each candidate, you will receive a detailed report with skills breakdown and benchmarks to shortlist the top candidates from your pool.

What topics are covered in the Machine Learning Assessment Test?

  • 梯度下降

    梯度下降是一种优化算法广泛用于机器学习,以最大程度地减少模型的成本函数。它迭代地在最陡下降的方向上调节模型的参数,以找到最佳解决方案。衡量此技能有助于评估候选人通过基于梯度的方法实施和优化机器学习模型的能力。

  • 当机器学习模型也适合培训数据时,会发生过度适应和不适合

    过度拟合紧密地,导致对看不见的数据的概括和性能不佳。另一方面,当模型太简单并且无法捕获数据中的基本模式时,就会发生不足。评估候选人对过度拟合和不足的理解有助于评估他们在模型复杂性方面的知识,并能够找到适当平衡以实现最佳性能的能力。

  • 支持向量机

    支持向量机(SVM)是用于分类和回归任务的监督学习算法。他们找到了一个最佳的超平面,可以将不同的类别分开或预测连续值。该技能的测量有助于招聘人员评估候选人在使用SVM方面的能力及其处理线性和非线性分类或回归问题的能力。

  • 偏见和方差

    偏见是指由模型过于简单的假设引入的错误,而方差衡量模型对训练数据中波动的敏感性。这两个概念有助于理解不足和过度拟合之间的权衡。评估候选人对偏见和差异的了解,招聘人员可以评估他们对模型性能的理解,并能够微调模型以获得更好的结果。

  • 交叉验证

    交叉验证是一种用于评估机器学习模型的性能和泛化功能的技术。它涉及将数据分为多个子集进行培训和测试,从而对模型的性能进行更强大的评估。评估候选人的交叉验证知识有助于确定他们在模型评估方面的专业知识以及避免过度绩效估算的能力。

  • 监督学习

    监督学习是一项机器学习任务,是一项机器学习任务。一个模型从标记的数据中学习以进行预测或分类。它涉及该模型旨在预测的明确目标变量。评估这项技能有助于衡量候选人对监督学习算法的理解及其将其应用于各种预测任务的能力。

  • 无监督的学习

    无监督的学习是一项机器学习任务,其中模型是一个模型学习从未标记的数据中找到没有特定目标变量的模式或结构。该技能衡量了候选人对无监督学习算法的熟悉,例如聚类和降低维度及其从非结构化数据中提取有意义的见解的能力。

  • 群集</h4>这些基于它们的特征或相似性,将相似的数据分组在一起。它有助于识别数据中的自然结构或类别。评估候选人的聚类算法知识意味着他们在探索数据中的模式以及将数据集细分为有意义的群集中进行进一步分析的能力。</p> <h4>降低维度的降低</p> <p>降低维度的降低是降低维度的过程。减少机器学习模型中输入变量/功能的数量。它通过在保留基本信息的同时删除多余或无关的功能来帮助简化复杂的数据集。评估此技能使招聘人员能够评估候选人对特征选择技术的理解及其提高模型性能和可解释性的能力。</p> <h4>模型评估

    模型评估是评估性能和性能的过程机器学习模型的质量。它涉及使用各种指标和技术来衡量模型概括地看不见数据的程度。评估这项技能有助于招聘人员确定候选人在评估和比较不同模型以及为给定任务选择最合适的模型的能力及其能力。

  • 功能工程

    功能工程是该过程创建新功能或转换现有功能以提高机器学习模型的性能。它涉及选择,创建或修改变量以更好地表示数据中的基本模式。衡量此技能使招聘人员能够通过有见地的功能工程技术评估候选人在增强模型的预测能力方面的专业知识。

  • Full list of covered topics

    The actual topics of the questions in the final test will depend on your job description and requirements. However, here's a list of topics you can expect the questions for 机器学习评估测试 to be based on.

    线性回归
    普通最小二乘
    梯度下降
    随机梯度下降
    批处理梯度下降
    山脊回归
    拉索回归
    多项式回归
    正则化
    过度拟合
    不足
    支持向量机
    内核技巧
    超平面
    软边缘
    硬利润
    偏见
    方差
    交叉验证
    k折交叉验证
    一对一的交叉验证
    保留方法
    监督学习
    分类
    回归
    决策树
    随机森林
    天真的贝叶斯
    k-near最邻居
    神经网络
    无监督的学习
    聚类
    k均值
    分层
    dbscan
    减少维度
    PCA(主要组件分析)
    LDA(线性判别分析)
    T-SNE(T分布的随机邻居嵌入)
    模型评估
    准确性
    精确
    记起
    F1得分
    ROC曲线
    AUC(曲线下方的区域)
    混淆矩阵
    功能工程
    数据转换
    功能缩放
    虚拟变量
    可变交互
    处理丢失的数据
    异常值检测

What roles can I use the Machine Learning Assessment Test for?

  • 机器学习开发人员
  • 机器学习工程师
  • 数据科学家
  • 数据分析师
  • 人工智能工程师
  • 数据工程师
  • 业务分析师
  • 研究科学家
  • 统计分析师
  • 数据挖掘专家

How is the Machine Learning Assessment Test customized for senior candidates?

For intermediate/ experienced candidates, we customize the assessment questions to include advanced topics and increase the difficulty level of the questions. This might include adding questions on topics like

  • 实施决策树和随机森林进行分类任务
  • 应用合奏方法,例如装袋和提升以提高模型性能
  • 了解神经网络的概念和应用
  • 为复杂任务实施深度学习模型
  • 利用自然语言处理技术进行文本分类和情感分析
  • 将建议系统应用于个性化建议
  • 了解强化学习的概念和应用
  • 实施时间序列分析以预测未来趋势
  • 利用异常检测技术来识别数据中的异常模式
  • 应用转移学习以利用预训练模型的知识
Singapore government logo

招聘经理认为,通过小组面试中提出的技术问题,他们能够判断哪些候选人得分更高,并与得分较差的候选人区分开来。他们是 非常满意 通过 Adaface 筛选入围的候选人的质量。


85%
减少筛查时间

Machine Learning Hiring Test 常见问题解答

机器学习测试包括什么类型的问题?

这项就业前机器学习测试包括基于方案的问题,这些问题要求候选人证明其能力:

  • 准备机器学习算法的数据
  • 使用ML算法,例如逻辑回归,支持向量机,决策树和随机森林进行分类
  • 构建聚类算法
  • 为特定用例提出最合适的算法
  • 估计学习算法的性能

该测试或评估可以用于高级机器学习工程师角色吗?

对于高级机器学习工程师,您可以请求自定义测试。在48小时内,我们的主题专家将根据您的职位描述和资历级别自定义评估。除基本面外,该测试还将重点介绍候选人的能力:

  • 结构ML项目
  • 确定各种机器学习算法的缺点
  • 设计数据清洁和数据标记过程
  • 选择适当的评估指标以提高模型性能
  • 评估硬件性能对机器学习算法的影响

我可以将多个技能结合在一起,为一个自定义评估吗?

是的,一点没错。自定义评估是根据您的职位描述进行的,并将包括有关您指定的所有必备技能的问题。

您是否有任何反交换或策略功能?

我们具有以下反交易功能:

  • 不可解决的问题
  • IP策略
  • Web Protoring
  • 网络摄像头Proctoring
  • 窃检测
  • 安全浏览器

阅读有关[Proctoring功能](https://www.adaface.com/proctoring)的更多信息。

如何解释考试成绩?

要记住的主要问题是评估是消除工具,而不是选择工具。优化了技能评估,以帮助您消除在技术上没有资格担任该角色的候选人,它没有进行优化以帮助您找到该角色的最佳候选人。因此,使用评估的理想方法是确定阈值分数(通常为55%,我们为您提供基准测试),并邀请所有在下一轮面试中得分高于门槛的候选人。

我可以使用该测试的经验水平?

每个ADAFACE评估都是为您的职位描述/理想候选角色定制的(我们的主题专家将从我们的10000多个问题的图书馆中选择正确的问题)。可以为任何经验级别定制此评估。

每个候选人都会得到同样的问题吗?

是的,这使您比较候选人变得容易得多。 MCQ问题的选项和问题顺序是随机的。我们有[抗欺骗/策略](https://www.adaface.com/proctoring)功能。在我们的企业计划中,我们还可以选择使用类似难度级别的问题创建多个版本的相同评估。

我是候选人。我可以尝试练习测试吗?

不,不幸的是,我们目前不支持实践测试。但是,您可以使用我们的[示例问题](https://www.adaface.com/questions)进行练习。

使用此测试的成本是多少?

您可以查看我们的[定价计划](https://www.adaface.com/pricing/)。

我可以免费试用吗?

我刚刚搬到了一个付费计划。我如何要求自定义评估?

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