矩阵多元回归实现问题求助

Hey Franz, sorry to hear you're stuck with that wonky regression result! It's super frustrating when one model works perfectly but the next throws out values way outside your expected 0-1 range. Let's dig into some key areas to help you track down the issue:

• Double-check if you're using the right model for your target variable
  If your second example's target is a probability (0-1) or a proportion, plain vanilla multivariate linear regression isn't the right tool here. Linear regression has no built-in constraints to keep outputs between 0 and 1—so it's totally normal for it to spit out extreme values in this case. You should switch to a logistic regression (for binary probability outcomes) or Beta regression (for continuous proportion data) instead, since these models use link functions to clamp outputs to your desired range.

• Audit your input data for anomalies

  • Look for outliers in your independent variables: A single extreme value (like a predictor that's 1000x larger than all others) can blow up regression coefficients and lead to wild predictions.
  • Verify your target variable's values: Did you accidentally scale it up (e.g., turning 0.5 into 50) or import the wrong column entirely? Even a tiny data entry mistake here can cause huge issues.
  • Check feature scaling: If your first example used normalized/standardized features but the second didn't, the mismatched predictor scales could throw off your model's calculations.

• Compare your code line-by-line between the two examples

  • Did you mix up dependent and independent variables in the second model? It's an easy mistake to make, and it would definitely lead to nonsensical predictions.
  • Print out the regression coefficients for the second model—if any coefficient is drastically larger than those in your first model, that's a red flag. A huge coefficient means even a small change in that predictor will swing your prediction way out of bounds.
  • Look for missing preprocessing steps: Did you forget to handle missing values, encode categorical variables, or split your data correctly in the second example?

• Validate if linear regression assumptions hold for your second dataset
  Linear regression assumes your target variable is continuous and normally distributed. If your second target is binary, a proportion, or has a heavily skewed distribution, those assumptions are violated. When that happens, linear regression will produce unreliable predictions that often fall outside logical ranges.

• Check your prediction code for errors
  Did you add an extra multiplier, offset, or transformation to the second model's predictions that you didn't use in the first? Even a tiny mistake here (like predictions * 200 instead of predictions) could push values way above 1.

Hopefully one of these leads helps you track down the issue!

内容的提问来源于stack exchange，提问作者Franz