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Mindforge ML | Unit 4 – Podcast 03_Title: Decision Trees and K-Nearest Neighbors Explained
Supervised learning begins with intuitive and interpretable models.
This episode explains Decision Trees and K-Nearest Neighbors — two fundamental supervised learning techniques based on rule splitting and distance measurement.
Key topics:
Decision Tree: Entropy, Information Gain and splitting logic.
Overfitting: Tree depth and pruning concept.
KNN: Distance-based classification.
Choosing K: Bias–variance balance.
This episode builds practical algorithm intuition before moving to margin-based methods.
Series: Mindforge ML
Produced by: Chatake Innoworks Pvt. Ltd.
Initiative: MindforgeAI
View all episodes
By CI CodesmithSupervised learning begins with intuitive and interpretable models.
This episode explains Decision Trees and K-Nearest Neighbors — two fundamental supervised learning techniques based on rule splitting and distance measurement.
Key topics:
Decision Tree: Entropy, Information Gain and splitting logic.
Overfitting: Tree depth and pruning concept.
KNN: Distance-based classification.
Choosing K: Bias–variance balance.
This episode builds practical algorithm intuition before moving to margin-based methods.
Series: Mindforge ML
Produced by: Chatake Innoworks Pvt. Ltd.
Initiative: MindforgeAI