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This presentation provides a comprehensive overview of decision trees and their role in supervised learning. It covers key concepts such as supervised learning, decision tree construction, overfitting, evaluation metrics, and practical considerations. The presentation also explores different types of learning, including supervised, unsupervised, and reinforcement learning, and discusses the importance of understanding implementation details for building effective models.
Typology: Schemes and Mind Maps
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This presentation explores decision trees and their crucial role in machine learning. We will cover supervised learning and its wide array of applications. Understanding implementation details is key to building effective models.
Our agenda includes learning from examples, decision tree construction, overfitting, evaluation metrics, and practical considerations.
Defined by labeled datasets consisting of input and desired output pairs. Used for classification and regression tasks.
Algorithms include Decision Trees, Support Vector Machines, and Neural Networks.
Focuses on discovering patterns in unlabeled data through clustering and dimensionality reduction. Algorithms: K-Means, Hierarchical Clustering, Autoencoders
Involves learning through interaction with an environment. The algorithm receives rewards or penalties for its actions.
Algorithms: Q-Learning, SARSA, Deep Q-Networks
The tree learns the training data too well, including noise. Generalization on new data becomes poor.
Simplifies the tree to improve generalization. Pre-pruning applies stopping criteria, while post- pruning removes branches.
Evaluates model performance on multiple subsets of data. Accurately estimates generalization error.
Cost Complexity Pruning and Reduced Error Pruning are key post-pruning techniques.
AUC-ROC measures the ability to distinguish between classes at different threshold settings. MSE, RMSE, MAE, and R-squared are key regression metrics.
Overall correctness. Misleading with imbalanced datasets.
Ability to avoid false positives.
Ability to find all positive instances.
Harmonic mean of precision and recall.
