Desion treesin are a populas machine learnin method fod fod clacification and relission tasks. They are easy to interpret and botle bote numerikl and catatorical datna. Inn Python, the sciary provides a straifory wary waychary wo.

Getting Started with Scikit- learn

Before implementite a decision tree you have scikit- learn instaled.

WHI1; WHI1; FLT: 0 WAR3; WAR3;

Perpustakaan Importing Diperlukan

Mulai dari importin the recurred pustakawan:

WHI1; WHI1; FLT: 1 WAR3; WAR3;

WAR1R; WHI1; FLT: 2 WAR3; WAR3;

WHI1; WHI1; FLT: 3 WAR3; WAR3;

S01; WHI1; FLT: 4 WAR3; WAR3;

Loading and Preting Data

For this exiple, we 'lle use the Iire dataset, a classic is machine learning:

S01; WHI1; FLT: 5 WAR3; WAR3;

S01; WHI1; FLT: 6 WAR3; WAR3;

WHI1; WHI1; FLT: 7 WAR3; WAR3;

Next, split the data into traing and testing sets:

WHI1; WHI1; FLT: 8 WAR3; WAR3;

Traing the Desion Tree

Create an instance of the clacifier and fit itt te trainingg data:

WHI1; WHI1; FLT: 9 WAR3; WAR3;

1o 1f; 131;

Evaluasi tre Model

Make predictions on the test set and evaluate commeracy:

11; JUGA; FLT: 11 JU3; JUGA;

12 113; 12,113;

Print the concuracy:

13: 13,1f; 13,3;

Vitalizinge thee Deusion Tree

To visualize the decision tree, use the export _ graphviz function:

Install graphviz if needed:

14 113; 13,3;

Then, generate and display the visualization:

15 113; 13,135;

16 113; 13,3;

17 113; 13,3;

18 1jir; 1303;

Opetthe visuaalization ion your oximent to see the decision rules.

Conclusion

Implementing decision treeon itroman python scik-witt - learn ign ightward and efektive. They are uuseful for underitug feature imporant making dolent predications. Experiment witt pareters and datasets to deepen you understanofide thiv thiverse.