Suppose that in a two
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dimensional space a decision tree model has been built for a twoclass classification problem. The decision boundaries as shown in the figure below,where x and y donate the two axes of the space, and x=0,y=0. Suppose that five samples, as shown in the table below, are used to testify the performance of the classifier.

Question

Suppose that in a two
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dimensional space a decision tree model has been built for a twoclass classification problem. The decision boundaries as shown in the figure below,where x and y ﻿donate the two axes of the space, and x>=0,y>=0. ﻿Suppose that five samples, as shown in the table below, are used to testify the performance of the classifier.

Accepted Answer

It seems there is a missing piece of information in the student's question, specifically the figure showing the decision boundaries and the table with the five samples. Without this information, I cannot provide a specific answer regarding the performance of the classifier on the given samples. However, I can explain the general process of building and evaluating a decision tree model for a two-class classification problem in a two-dimensional space. ∻Building a Decision Tree Model∻ ‖ ▪ ⋮ Collect Data: Gather a dataset with features and labels for training. ‖ ‖ ▪ ⋮ Preprocess Data: Clean the dataset, handle missing values, and encode categorical variables if necessary. ‖ ‖ ▪ ⋮ Split Data: Divide the dataset into training and testing sets. ‖ ‖ ▪ ⋮ Train Model: Use the training set to build the decision tree, making splits based on criteria like Gini impurity or information gain. ‖ ‖ ▪ ⋮ Prune Tree: Optionally, prune the tree to avoid overfitting by removing sections that provide little power to classify instances. ‖ ∻Evaluating a Decision Tree Model∻ ‖ ▪ ⋮ Confusion Matrix: Create a confusion matrix to visualize true positives, false positives, true negatives, and false negatives. ‖ ‖ ▪ ⋮ Accuracy: Calculate the accuracy of the model as the number of correct predictions divided by the total number of predictions. ‖ ‖ ▪ ⋮ Precision and Recall: Compute precision and recall to understand the model's performance with respect to false positives and false negatives. ‖ ‖ ▪ ⋮ F1 Score: Use the F1 score to find the harmonic mean of precision and recall, providing a balance between the two. ‖ ‖ ▪ ⋮ Cross-Validation: Perform cross-validation to ensure that the model's performance is consistent across different subsets of the data. ‖ ⚹ Without the specific decision boundaries and sample data, I cannot provide a detailed analysis of the classifier's performance on the given samples. However, the general steps outlined above are crucial for building and evaluating a decision tree model in a two-dimensional space for a two-class classification problem⚹

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