DecisionTreeClassification i machine learing

1. Decision Tree Classification (DTC)
• What is Decision Tree Classification (DTC)
• How does the DTC work
• The Issue of Overfitting; how to tackle it
• How do we optimize a Decision Tree:
Pruning: parameters used in Pruning
• How to decide most effective query:
Feature/ Attribute Importance Analysis
• Applications / Use

2. Decision Tree Classification
 It is a Supervised Learning algorithm
 Based on evaluating and maximizing
information gain from developing classes based
on various properties of the attributes
 used for classification and regression modeling.
 Regression is a method used for predictive
modeling,
 so these trees are used to either classify data or
predict what will come next.
 The name is derived from the similarity of the
pictorial representation of a Decision Tree
algorithm with a real tree with its root upside-
down tree, and branches off to demonstrate
various outcomes.

3. Decision Tree Classification

4. Decision Tree Classification Process

5. Decision Questions are based on Information Gain

6. What is Decision Tree Classification
https://www.coursera.org/articles/decision-tree-machine-learning

7. What is Decision Tree Classification
https://www.coursera.org/articles/decision-tree-machine-learning

8. What is Decision Tree Classification
https://www.coursera.org/articles/decision-tree-machine-learning

9. What is Decision Tree Classification
https://www.coursera.org/articles/decision-tree-machine-learning

10. What is Decision Tree Classification
https://www.coursera.org/articles/decision-tree-machine-learning

11. The Case of Over-fitting
https://www.coursera.org/articles/decision-tree-machine-learning

12. The Case of Over-fitting
https://www.coursera.org/articles/decision-tree-machine-learning

13. How to tackle Over-fitting: Pruning
 Pre-pruning
 Post-Pruning
 The Decision Tree module of the scikit-learn,
implements only Prepruning
 Pre-pruning parameters in DecisionTreeClassifier has
3 pre-pruning parameters:
 max_depth
 Max_leaf_nodes
 Min_samples_leaf
https://www.coursera.org/articles/decision-tree-machine-learning

14. The Case of Over-fitting
https://www.coursera.org/articles/decision-tree-machine-learning

15. Feature (Attribute) Importance Analysis
https://www.coursera.org/articles/decision-tree-machine-learning

16. What is Decision Tree Classification

17. What is Decision Tree Classification

18. What is Decision Tree Classification