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In Classification, Data Science Recipes, Machine Learning, and Python
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Published Date: June 2, 2018

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In the ZIP file, you will get a) the self instructed recipe (code) - Python script (DSR-028.py), b) the dataset used in the recipe - Pima Indian Diabetes dataset (pima.indian.diabetes.data.csv). and c) the predicted outcome of the model (finalResult.csv). 

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Binary Classification using CatBoost in Python: Manual and Automatic Parameter Tuning

In this Data Science Recipe, the reader will learn:

  1. How to organise a Predictive Modelling Machine Learning project step by step.
  2. What are the different steps in Predictive Modelling and Applied Machine Learning.
  3. How to summarise and present feature variables in Predictive Modelling (Descriptive statistics).
  4. How to visualise features through histogram, density plot, box plot and scatter matrix.
  5. How to find correlations among features variables.
  6. How to visualise target variables.
  7. How to do data analysis for feature and target variables.
  8. How to utilise Catboost, sklearn and pandas packages in Python.
  9. How to implement Boosting Algorithms for Binary Classification Algorithm in Python.
  10. How to implement CatBoost for Binary Classification Algorithm in Python.
  11. How to setup CatBoost hyper-parameters: manual and automatic tuning in Python.
  12. How to setup RandomSearchCV and GridSearchCV for parameter tuning in Python.
  13. How to compare Algorithms with Accuracy and Kappa in Python.

 

 



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Tagged In catboost, data science recipe, Gradient Boosting, machine learning recipe, python, scikit-learn, and sklearn.

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