From e9ea7dccd669b26b4f9acfa628a314edf0bd87e8 Mon Sep 17 00:00:00 2001 From: Mike Laverick Date: Sat, 25 May 2024 15:50:51 +1200 Subject: [PATCH] fix another heading --- _episodes/03-classification.md | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/_episodes/03-classification.md b/_episodes/03-classification.md index 395ce20..00cfc33 100644 --- a/_episodes/03-classification.md +++ b/_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. - +{: .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! - -{: .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! \ No newline at end of file