Computational Complexity || Decision Tree || ID3 || CART
Computational Complexity of Decision Tree Algorithms | Machine Learning Explained
In this video, we break down the computational complexity of decision tree algorithms—a core concept in machine learning and data science. Whether you're preparing for exams, brushing up for interviews, or just exploring AI, understanding how decision trees scale with data is essential.
🔍 Topics Covered:
What is a decision tree?
Time complexity of training vs prediction
Factors affecting complexity: number of features, samples, and tree depth
Complexity of popular algorithms like ID3, C4.5, CART
Optimizations and trade-offs
Real-world implications and performance tips
🧮 Key Concepts:
Training complexity: O(n·m·log n) to O(n²·m) depending on implementation
Prediction time: O(depth of the tree)
Pruning and feature selection impact
Why decision trees can be both powerful and computationally expensive
📌 Whether you're using scikit-learn, XGBoost, or building your own models from scratch, this video gives you the theoretical and practical knowledge to understand how decision trees perform under the hood.
👉 Don't forget to like, subscribe, and drop your questions in the comments!
#DecisionTree #MachineLearning #ComputationalComplexity #DataScience #AI #MLAlgorithms #ID3 #CART #XGBoost
Видео Computational Complexity || Decision Tree || ID3 || CART канала Dr. RAMBABU PEMULA
In this video, we break down the computational complexity of decision tree algorithms—a core concept in machine learning and data science. Whether you're preparing for exams, brushing up for interviews, or just exploring AI, understanding how decision trees scale with data is essential.
🔍 Topics Covered:
What is a decision tree?
Time complexity of training vs prediction
Factors affecting complexity: number of features, samples, and tree depth
Complexity of popular algorithms like ID3, C4.5, CART
Optimizations and trade-offs
Real-world implications and performance tips
🧮 Key Concepts:
Training complexity: O(n·m·log n) to O(n²·m) depending on implementation
Prediction time: O(depth of the tree)
Pruning and feature selection impact
Why decision trees can be both powerful and computationally expensive
📌 Whether you're using scikit-learn, XGBoost, or building your own models from scratch, this video gives you the theoretical and practical knowledge to understand how decision trees perform under the hood.
👉 Don't forget to like, subscribe, and drop your questions in the comments!
#DecisionTree #MachineLearning #ComputationalComplexity #DataScience #AI #MLAlgorithms #ID3 #CART #XGBoost
Видео Computational Complexity || Decision Tree || ID3 || CART канала Dr. RAMBABU PEMULA
decision tree computational complexity machine learning data science decision tree algorithm time complexity space complexity ID3 CART C4.5 tree depth training complexity prediction time scikit-learn XGBoost AI algorithms ML algorithms supervised learning algorithm analysis pruning feature selection big O notation machine learning tutorial data structures performance optimization decision tree complexity
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21 мая 2025 г. 17:31:41
00:04:48
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