GoCoding

Part B: Stopping Conditions Introduction In the preceding section, we introduced an algorithm designed to construct a decision tree. This algorithm incorporates a specific feature known as a  stopping condition.  Question: Question:  If we don’t terminate the decision tree algorithm manually, what will the leaf nodes of the decision tree look like? Show Answer Answer:  The tree will continue to grow until each leaf node contains  exactly one training point  and the model attains  100%  training accuracy. As you might remember from our previous course, 100% accuracy is a bad thing! It almost certainly means that we have overfit our data.  Question: Question:  How can we prevent this from happening? Show Answer Answer:  Stop the tree from growing. Common Stopping Conditions The most common stopping criterion involves restricting the  maximum depth  ( max_depth ) of the tree. The following diagram illustrates a decision tree ...

  Entropy Assume we have P  predictors  and K  classes . Suppose we select the k th  predictor and split a region along the threshold  . We can assess the quality of this split by measuring the  entropy of the class distribution  in each newly created region by calculating: Note:  We are actually computing the conditional entropy of the distribution of training points amongst the K classes given that the point is in region r. The entropy calculation here yields a value of 1.38, compared to a misclassification rate of 0.38 and a Gini index of 0.47. We can now try to find the predictor p and the threshold  t p  minimizes the  weighted average entropy  over the two regions: Where N r  is the number of training points inside of region R r .

 Advanced Data Visualization Techniques in Python: Focus on Advanced Matplotlib Techniques 1. Overview Matplotlib adalah salah satu pustaka pemetaan yang paling banyak digunakan di Python, terkenal karena fleksibilitasnya dan berbagai pilihan visualisasi yang komprehensif. Ini menjadi dasar bagi banyak pustaka visualisasi lainnya, seperti Seaborn dan Plotly. Kemampuan Matplotlib untuk membuat plot statis, animasi, dan interaktif menjadikannya sangat penting bagi para ilmuwan data, analis, dan pengembang yang bertujuan untuk menyampaikan wawasan data dengan efektif. Kemampuan kustomisasi yang luas memungkinkan pengguna untuk menyesuaikan visualisasi sesuai dengan kebutuhan spesifik, meningkatkan baik kejelasan maupun daya tarik estetika. 2. Advanced Techniques Berikut adalah tiga teknik Matplotlib tingkat lanjut yang secara signifikan meningkatkan visualisasi data: a. Subplots and GridSpec for Complex Layouts Description:  Subplot memungkinkan pembuatan beberapa plot dalam satu...