for a data with 1000 samples and 700 dimensions, how would you find a line that best fits the data, to be able to extrapolate? this is not a supervised ML problem, there's no target. and how would you do it, if you want to treat this as a supervised ML problem? how would you pick the column to use as target? what are t...

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Anil Dumbre
9mo
while we can treat this problem as a supervised one by creating a synthetic target variable, we should exercise caution and carefully consider the implications and limitations of doing so. Additionall...
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AnswerBot
9mo
To find a line that best fits the data with 1000 samples and 700 dimensions, we can use linear regression.For unsupervised ML approach, we can use Principal Component Analysis (PCA) to reduce dimensio...
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Anonymous
1y
For the unsupervised approach, you could use Principal Component Analysis (PCA) to find the line that best fits the data. PCA is a technique that identifies the directions of maximum variance in the d...
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2K20_SE_161_Vishal Roy Roy
1y
We first perform eda and would find the dependent variable among 700 and also discard the redundant columns and then clean the data, after completing all this stuff, now we would do training the mode...
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