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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 ...

 Java code appears to be a solution to the ArrayChallenge problem, and it follows the recursive approach to check if any combination of numbers (excluding the largest number) can add up to the largest number in the array. However, there is a minor issue in your code that needs to be corrected. The ArrayChallenge method is defined to take an integer array as an argument, but in your main method, you are reading a single line of input as a string and passing it to ArrayChallenge . You should parse the input string to an integer array first. Here's the corrected version of your code: import java . util .* ; import java . io .* ; class Main {   public static boolean ArrayChallenge ( int [] arr ) {     // code goes here       int max = Integer . MIN_VALUE ;     int sum = 0 ;     for ( int num : arr ) {       if ( num > max ) {         max = num ;       }   ...

 String Challenge Have the function StringChallenge(sen) take the sen parameter being passed and return the longest word in the string. If there are two or more words that are the same length, return the first word from the string with that length. Ignore punctuation and assume sen will not be empty. Words may also contain numbers, for example "Hello world123 567" Once your function is working, take the final output string and concatenate it with your ChallengeToken, and then replace every fourth character with an underscore. Your ChallengeToken: 386emsol49 Examples Input: "fun&!! time" Output: time Final Output: tim_386_mso_49 Input: "I love dogs" Output: love Final Output: lov_386_mso_49 -------------------------------- Java code to implement the StringChallenge function as described, along with the ChallengeToken concatenation and character replacement: public class Main {     public static void main ( String [] args ) {         System . ...

 Advanced Data Visualization in Python: Seaborn for Statistical Data Visualization 1. Overview of Seaborn Seaborn is a Python data visualization library built on top of Matplotlib, designed specifically for creating attractive and informative statistical graphics. It provides a high-level interface for drawing plots that are easy to interpret and useful for exploring and understanding data. Seaborn integrates well with Pandas, allowing users to create complex visualizations with minimal code, making it a preferred choice for statistical data analysis. 2. Key Features of Seaborn Built-in Themes : Seaborn comes with several built-in themes for styling Matplotlib graphics, which enhances the aesthetics of plots without the need for extensive customization. Statistical Estimation : Seaborn has functions like sns.barplot and sns.pointplot that perform statistical estimation while plotting. For instance, it can automatically compute confidence intervals for a given dataset. Complex ...

 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...