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Arthur Danjou
2024-11-27 11:59:54 +01:00
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content/portfolio/what-is-machine-learning.md
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@@ -81,8 +81,8 @@ For regression problems, the **R² score** measures the proportion of the target
$$R2 = 1 - \frac{\text{SS}_{\text{residual}}}{\text{SS}_{\text{total}}}$$ where: $$R2 = 1 - \frac{\text{SS}_{\text{residual}}}{\text{SS}_{\text{total}}}$$ where:
- $$\text{SS}\_{\text{residual}}$$ : Sum of squared residuals between actual and predicted values. - $$\text{SS}_{\text{residual}}$$ : Sum of squared residuals between actual and predicted values.
- $$\text{SS}\_{\text{total}}$$ : Total sum of squares relative to the target’s mean. - $$\text{SS}_{\text{total}}$$ : Total sum of squares relative to the target’s mean.
A $$R^2$$ close to 1 indicates a good fit. A $$R^2$$ close to 1 indicates a good fit.