title: Data selection approach for Random forest regression with autocorrelation
body: <p>I am new to machine learning and I have been looking for answers to some questions I have. I am building a yield prediction model with a random forest regression model. I have around 27 farmer fields with yield map data. As my input variables, I use several indices. I used 10 000 random points to extract these index values. So far I used data from 20 fields (around 7000 points) to develop my RF model and data from 7 fields (3000 points) to validate my model. The 7 fields I chose for validation are located separately from the testing 20 fields. Total area of all my fields is 27km2) I have some questions in regards to this.</p>
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  <li><p>How does spatial autocorrelation affect my model? (I used moran's index to test for spatial autocorrelation and the results show that my datapoints are clustered, I tried reducing the number of points up to 50 points and they are still clustered. Therefore I decided to ignore autocorrelation and move forward with 10 000 data points).</p>
  </li>
  <li><p>Is my approach used in selecting testing and validation data correct? (when I use this method, I noticed that the correlation in validation highly depends on the data I select for validation. I think that this might be due to differences in yield distribution in each field).</p>
  </li>
  <li><p>Can I randomly choose 70% of data from all fields for testing and 30% for validation without considering the field? (I tried this method too and the results are better than field-based data extraction. But I don't know if this is valid. As my data has spatial autocorrelation)</p>
  </li>
  <li><p>I just decided that I would use 10 000 points. Is there any technique I should use to determine the most appropriate number of points for a random forest regression?</p>
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  </ol>
  <p>I appreciate it if someone can help me here and/or share with me where I can read up on this.</p>
  <p>Thanks in Advance.</p>