fix another heading

_episodes/03-classification.md

@@ -129,7 +129,7 @@ clf.predict(x_test)
 
 > ## Hyper-parameters: parameters that tune a model
 > 'Max Depth' is an example of a *hyper-parameter* for the decision tree model. Where models use the parameters of an observation to predict a result, hyper-parameters are used to tune how a model works. Each model you encounter will have its own set of hyper-parameters, each of which affects model behaviour and performance in a different way. The process of adjusting hyper-parameters in order to improve model performance is called hyper-parameter tuning.
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+{: .callout}
 
 We can conveniently check how our model did with the .score() function, which will make predictions and report what proportion of them were accurate:
 
@@ -300,6 +300,4 @@ plt.show()
 
 ![Classification space generated by the SVM model](../fig/e3_svc_space.png)
 
-While this SVM model performs slightly worse than our decision tree (95.6% vs. 98.5%), it's likely that the non-linear boundaries will perform better when exposed to more and more real data, as decision trees are prone to overfitting and requires complex linear models to reproduce simple non-linear boundaries. It's important to pick a model that is appropriate for your problem and data trends!
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-{: .callout}
+While this SVM model performs slightly worse than our decision tree (95.6% vs. 98.5%), it's likely that the non-linear boundaries will perform better when exposed to more and more real data, as decision trees are prone to overfitting and requires complex linear models to reproduce simple non-linear boundaries. It's important to pick a model that is appropriate for your problem and data trends!