Comparing SVM and Logistic Regression with Outliers 🤔

Logistic Regression is pretty sensitive to outliers. 😬 They can really mess with the estimation of its coefficients, forcing it to find a linear boundary even if it's not ideal. Sometimes, a sigmoid function is used to help mitigate this. 🤓

SVMs also struggle with outliers. 😩 They can cause the decision boundary to stray far from the optimal hyperplane. To combat this, SVMs use "slack variables." 💪 These come with a penalty, and how SVM handles outliers depends on how this penalty is applied.

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Implementing a Simple kNN Algorithm in Code 💻

kNN is a versatile algorithm used for both classification and regression. 🤩 Here's the basic idea:

1. Calculate the distance between your new data point and all the training data points. 📏
2. Find the k nearest neighbors based on these distances. 👯
3. For classification, take a "majority vote" of the neighbors to determine the class of the new data point. 🗳️

What's a "Pure" Node in a Decision Tree? 🌳

In a decision tree, a "pure" node is like hitting the jackpot! 🥳 It means the Gini Index is 0, indicating that all elements in that node belong to the same class. 🎉

When a node is pure, you've reached a leaf node. 🍃 This leaf node represents the final output or class label. 🏷️

Space and Time Complexity of Hierarchical Clustering ⏰💾

Hierarchical Clustering can be quite demanding on resources, especially with large datasets. 😥

• Space Complexity: Because it needs to store a similarity matrix, the space complexity is O(n²), where n is the number of observations. 🤯
• Time Complexity: With n iterations and the need to update and store the proximity matrix in each iteration, the time complexity is O(n³). 😫

ENJOY LEARNING! 👍👍✨

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