Amazon人工智能面试真题

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面试题
全部(39)
ML Domain(39)
全部(39)
ML Domain(39)
1.Employee Contacts
2.Evaluate Model
3.Coeficient of Regression
4.降维
5.Loss
6.Decision Tree
7.Metric
8.Metric
9.Class imbalance
10.Outlier detection
11.Graph coloring
12.SVM
13.PCA
14.Feature Selection
15.NLP相关问题
16.ML基础知识
17.Dropout
18.Batch Norm
19.Gradient Descent
20.Regularization
21.SVM
22.Bias/Variance
23.Gradient Descent
24.Transformer相关问题
25.Transformer相关问题
26.RNN相关问题
27.RNN相关问题
28.RNN相关问题
29.时间序列
30.KNN
31.Waitlist
32.垒砖块
33.Shorten Sentence
34.Text Classification
35.Metric计算
36.树模型区别
37.Bagging
38.Imbalanced Data
39.Bias and Variance
1. Employee Contacts
 | Data Frame column: Month-Key, Date-Key, Employee ID, Contact channel, Department
 Rows:2019-09-01,2019-09-25,’E12345’,Email,Ship
 1. Find how many times an employee contacts each month
 2. Find the latest contact date in each month for each employee
 3. Create a new column 'status' when employee contacts more than 10 times per months
 ->"hi”,otherwise->"lo”
 4. lf you build a classification model to predict whether the employee will contact in the
 next month, how will you split the data to train, test, validation?
 Ans:
 iteration1: train: data between 2020-01 to 2020-05: test: data of 2020-06
 iteration2: train: data between 2020-01 to 2020-06; test: data of 2020-07
 iteration3: train: data between 2020-01 to 2020-07: test: data of 2020-08
 iteration4: train: data between 2020-01 to 2020-07: test: data of 2020-09
2. Evaluate Model
How will you evaluate model
3. Coeficient of Regression
 | The coeeficient difference between logistic regression and linear regression
4. 降维
什么是降维?PCA和NMF的区别在哪里?降维一般用的什么方法来Visualize? tSNE和UMAP的区别在哪里?
5. Loss
目标函数是什么?