c******t
发帖数: 8 | 1 You have dataset 1, with 100 explanatory variables, response and 100
observations. You want to construct a model for prediction, but 100
variables is too many. Penalized methods, such as LASSO can be used.
The question is: you have dataset 2, with same 100 explanatory variables,
but the sample size is 1000, no response, and the data come from similar
population. How to use dataset 2 to help you construct a prediction model
for dataset 1?
Thanks | B******5
发帖数: 4676 | 2 semi-supervised learning? | R*******c
发帖数: 249 | 3 他家不会还在招吧。。。
【在 c******t 的大作中提到】
: You have dataset 1, with 100 explanatory variables, response and 100
: observations. You want to construct a model for prediction, but 100
: variables is too many. Penalized methods, such as LASSO can be used.
: The question is: you have dataset 2, with same 100 explanatory variables,
: but the sample size is 1000, no response, and the data come from similar
: population. How to use dataset 2 to help you construct a prediction model
: for dataset 1?
: Thanks
| G*******s
发帖数: 10605 | 4 Principal Component?
【在 c******t 的大作中提到】
: You have dataset 1, with 100 explanatory variables, response and 100
: observations. You want to construct a model for prediction, but 100
: variables is too many. Penalized methods, such as LASSO can be used.
: The question is: you have dataset 2, with same 100 explanatory variables,
: but the sample size is 1000, no response, and the data come from similar
: population. How to use dataset 2 to help you construct a prediction model
: for dataset 1?
: Thanks
| L*****k
发帖数: 327 | 5 this is a transfer learning problem, hehe~~many ways to do it
en, the most straightforward way is, do unsupervised learning for data1+
data2(explanatory data only) together, like dimension reduction
【在 c******t 的大作中提到】
: You have dataset 1, with 100 explanatory variables, response and 100
: observations. You want to construct a model for prediction, but 100
: variables is too many. Penalized methods, such as LASSO can be used.
: The question is: you have dataset 2, with same 100 explanatory variables,
: but the sample size is 1000, no response, and the data come from similar
: population. How to use dataset 2 to help you construct a prediction model
: for dataset 1?
: Thanks
| l******0
发帖数: 73 | 6 我面试时也问了这个问题，请问这是哪家的经典？
【在 R*******c 的大作中提到】
: 他家不会还在招吧。。。
| o****o
发帖数: 8077 | 7 does it mean that use the information in dataset2 to prevent overfitting in
dataset1?
【在 L*****k 的大作中提到】
: this is a transfer learning problem, hehe~~many ways to do it
: en, the most straightforward way is, do unsupervised learning for data1+
: data2(explanatory data only) together, like dimension reduction
| s******0
发帖数: 1269 | 8 could you give more details about how to answer? | D******n
发帖数: 2836 | 9 it bears so many names.
Isnt it just like the reject-inference problem in risk modeling?
【在 B******5 的大作中提到】
: semi-supervised learning?
| L*****k
发帖数: 327 | 10 in the conceptual level, if you have data from dataset2, and there is a link
between dataset 1 & 2, then the knowledge you learn from 2 should be
helpful for tasks in dataset 1
in
【在 o****o 的大作中提到】
: does it mean that use the information in dataset2 to prevent overfitting in
: dataset1?
| L*****k
发帖数: 327 | 11 this is not a typical semi-supervised learning problem, which assumes the
explanatory data have the same distribution
【在 D******n 的大作中提到】
: it bears so many names.
: Isnt it just like the reject-inference problem in risk modeling?
| w********m
发帖数: 1137 | 12 Situation: n = p -> high dimension/low power
Action: PCA -> dimension reduction
Result: avoid overfitting and heteroskedasticity. | S*x
发帖数: 705 | 13 PCA
or
Proc varclus
on the 2nd dateset
Use the selected (new) variables from 2nd dataset, build model on first
dataset
【在 c******t 的大作中提到】
: You have dataset 1, with 100 explanatory variables, response and 100
: observations. You want to construct a model for prediction, but 100
: variables is too many. Penalized methods, such as LASSO can be used.
: The question is: you have dataset 2, with same 100 explanatory variables,
: but the sample size is 1000, no response, and the data come from similar
: population. How to use dataset 2 to help you construct a prediction model
: for dataset 1?
: Thanks
| A*******s
发帖数: 3942 | 14 need to ask more details to see what is of interest behind that question. my
speculation is that since it is a high dimensional problem, interviewers
may expect you say something like PCA. And the task is a typical semi-
supervised learning, so maybe "first clustering, then EM" methods are
somethings they are looking for. |
|
