**Machine Learning Essentials: Decision Trees**

English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 16m | 200 MB

Many data science specialists are looking to pivot toward focusing on machine learning. This course covers the essentials of machine learning, including predictive analytics and working with decision trees. Explore several popular tree algorithms and learn how to use reverse engineering to identify specific variables. Demonstrations of using the IBM SPSS Modeler are included so you can understand how decisions trees work. This course is designed to give you a solid foundation on which to build more advanced data science skills.

Topics include:

- Using the SPSS Modeler
- Building a CHAID model
- Adding a second model with C&RT
- Analysis notes
- Using a lift and gains chart
- Exploring algorithms
- Building a tree interactively
- The Bonferonni adjustment
- Handling nominal, ordinal, and continuous variables
- Examining the CHAID tree
- The Gini coefficient
- Weighing purity and balance
- Understanding pruning
- Examining the C&RT tree
- Applying stopping rules
- Using the Auto Classifier tuning trick

**Table of Contents**

1 Welcome

2 What you should know

3 Using the exercise files

4 Decision tree options in SPSS Modeler

5 Building a quick CHAID model

6 Adding a second model with C&RT

7 Analysis nodes

8 Lift and gains chart

9 What is an algorithm_

10 Chi-squared overview

11 Buliding a tree interactively

12 Bonferonni adjustment

13 What is level of measurement_

14 How CHAID handles nominal variables

15 How CHAID handles ordinal variables

16 How CHAID handles continuous variables

17 A quick look at the complete CHAID tree

18 What is the Gini coefficient_

19 How does C&RT weigh purity and balance_

20 How C&RT handles nominal, ordinal, and continuous variables

21 How C&RT handles missing data

22 Understanding pruning

23 A quick look at the complete C&RT tree

24 Stopping rules in CHAID and C&RT

25 Exhaustive CHAID

26 The Auto Classifier tuning trick

27 Next steps

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