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4 changes: 3 additions & 1 deletion data-analysis.md
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#### 1. (Given a Dataset) Analyze this dataset and tell me what you can learn from it.
#### 2. What is R2? What are some other metrics that could be better than R2 and why?
- R2 is the square of correlation between the observed target variable and the predicted target variable
- goodness of fit measure. variance explained by the regression / total variance
- the more predictors you add the higher R^2 becomes.
- the more predictors you add the higher R^2 becomes. This will always be biased to models with more features
- hence use adjusted R^2 which adjusts for the degrees of freedom 
- or train error metrics
- Akaike information criteria (AIC) which penalizes the model for having more predictors. A larger value indicates a worse fit
#### 3. What is the curse of dimensionality?
- High dimensionality makes clustering hard, because having lots of dimensions means that everything is "far away" from each other.
- For example, to cover a fraction of the volume of the data we need to capture a very wide range for each variable as the number of variables increases
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