title: supervised machine learning: relationship between number of data points and variables
body: <p>Let say. We have a dataset (in .csv format) for supervised machine learning. It has 60 data points (row of data), and each data point has 100 variables. </p>
  
  <p>Does it make sense that I train machine learning models using all 100 variables from 60 data points? To me, it seems that it is mathematically wrong. It like I solve an equation set that with 100 variables, but only 60 equations? </p>
  
  <p>In a dataset, if we have n variables, what is the minimal number of data points we need to train a machine learning model?</p>
  
  <p>Any statistic theory for this?</p>
  
  <p>Thank you very much.</p>