Instructions Use Google Colab to run this notebook: Decision-Tree Classifier Tutorial | Kaggle Objective: Learn implementation of decision tree Learn about Overfitting concept in Decision-Tree algorithm Learn different attribute selection measures
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Explore an online published machine learning project. Based on what you have learned in this course, compose a research report that includes the following: - What is the problem? - What is the type of machine learning? - What are the feature variables and target variables? - What data preprocessing was used? - How did the author explore the data? - What machine learning algorithms were used? - How was the model's performance evaluated? - What is the conclusion? Is it reasonable? - If you were the author, which part would you want to improve? Pick the following Kaggle project: - Titanic Survival
a. Ensemble classifiers have been quite successful in generating supervised learning systems that exhibit very high accuracies. Can you justify this assertion using a specific example? b. If the training examples are linearly separable, how many decision boundaries can separate positive from negative data points using SVM? Which decision boundary does the SVM algorithm calculate? c. Assume that there are three correlated classification models (A, B, and C), such that the models A, B, and C have a prediction accuracy of 75%, 80%, and 65%, respectively. What is the best possible way(s) of combining these models to optimize prediction accuracy? Justify your answer.
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