MFML 102 - Decision trees and SVMs compared


Summary

The video explains the difference between linearly separable and non-linearly separable data using yellow and green representations. It introduces decision trees by starting with features and discusses the placement of vertical lines to make decisions. Through an analogy of mixing cocktails, the video demonstrates achieving pure yellow and pure green mixes, illustrating how support vector machines work and the importance of feature engineering in solving problems efficiently. The approach of problem-solving without labels is introduced, emphasizing a logic-based method of 'if this then that'.


Linearly Separable vs Non-linearly Separable Data

Explanation of linearly separable and non-linearly separable data using yellow and green representations.

Understanding Decision Trees

Introduction to decision trees and how they work by starting with one of the features.

Placement of Vertical Lines

Discussion about determining the placement of vertical lines in decision trees.

Intimidating Equations

Mention of intimidating equations related to mixing cocktails as an analogy.

Concept of Mixing Cocktails

Explaining the concept of mixing cocktails in relation to metrics and concentrations.

Pure Mix of Yellow

Discussion on achieving a pure mix of yellow using the analogy of mixing cocktails.

Pure Mix of Green

Explanation of achieving the purest mix of green in the cocktail analogy.

Understanding Support Vector Machines

Illustration of how support vector machines work using the concept of tiling with smaller tiles.

Applying Feature Engineering

Discussion on solving problems through feature engineering and altering features in algorithms.

Feature Alteration in Algorithms

Explanation of altering features in algorithms to make them more favorable for specific methods.

No Labels Approach

Introduction to the approach of solving problems without labels and using the logic of 'if this then that'.

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