Decision Trees, Random Forests, AdaBoost & XGBoost in Python
Decision Trees and Ensembling techniques in Python. How to run Bagging, Random Forest, GBM, AdaBoost & XGBoost in Python
Created by Start-Tech Academy | 7 hours on-demand video
This Decision Trees, Random Forests, AdaBoost & XGBoost in Python course covers all the steps that one should take while solving a business problem through Decision tree. Most courses only focus on teaching how to run the analysis but we believe that what happens before and after running analysis is even more important i.e. before running analysis it is very important that you have the right data and do some pre-processing on it. And after running analysis, you should be able to judge how good your model is and interpret the results to actually be able to help your business.
What you’ll learn
- Get a solid understanding of decision tree
- Understand the business scenarios where decision tree is applicable
- Tune a machine learning model’s hyperparameters and evaluate its performance.
- Use Pandas DataFrames to manipulate data and make statistical computations.
- Use decision trees to make predictions
- Learn the advantage and disadvantages of the different algorithms
Recommended Course
Deep Learning Prerequisites: The Numpy Stack in Python (V2+)
Bayesian Machine Learning in Python: A/B Testing
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