Supervised Learning - Classification Fundamentals (Weeks 6)
The chapter focuses on two powerful classification techniques: Support Vector Machines (SVMs) and Decision Trees, exploring their principles, advantages, and detailed implementations. It emphasizes the significance of concepts such as hyperplanes, margins, kernel tricks, and the construction of decision trees along with challenges like overfitting. Finally, practical lab exercises provide hands-on experience in implementing and comparing these algorithms, enhancing understanding of their strengths and weaknesses.
Sections
Navigate through the learning materials and practice exercises.
What we have learnt
- Support Vector Machines (SVMs) are designed to find optimal boundaries in classification tasks.
- The margin maximization principle leads to better generalization and robustness in SVMs.
- Decision Trees provide intuitive, rule-based models that mirror human decision-making processes while requiring careful management to prevent overfitting.
Key Concepts
- -- Support Vector Machines (SVM)
- A supervised learning model used primarily for classification that finds the optimal separating hyperplane between classes.
- -- Hyperplane
- The decision boundary in SVMs that separates different classes in the feature space.
- -- Margin
- The distance between the hyperplane and the nearest data points from each class, which SVMs aim to maximize to improve classification performance.
- -- Kernel Trick
- A technique used in SVMs to enable the algorithm to work in a higher-dimensional space without explicitly computing coordinates, allowing for separation of non-linear data.
- -- Decision Tree
- A non-parametric supervised learning model that splits data into subsets based on feature tests, ultimately leading to a final classification.
- -- Gini Impurity
- A measure of the probability of misclassifying a randomly chosen element in the node, used to evaluate the quality of a split in a Decision Tree.
- -- Entropy
- A measure of disorder or uncertainty in the data, used to compute information gain when determining the optimal splits in Decision Trees.
Additional Learning Materials
Supplementary resources to enhance your learning experience.