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Binary Classification using CatBoost in Python: Manual and Automatic Parameter Tuning
In this Data Science Recipe, the reader will learn:
- How to organise a Predictive Modelling Machine Learning project step by step.
- What are the different steps in Predictive Modelling and Applied Machine Learning.
- How to summarise and present feature variables in Predictive Modelling (Descriptive statistics).
- How to visualise features through histogram, density plot, box plot and scatter matrix.
- How to find correlations among features variables.
- How to visualise target variables.
- How to do data analysis for feature and target variables.
- How to utilise Catboost, sklearn and pandas packages in Python.
- How to implement Boosting Algorithms for Binary Classification Algorithm in Python.
- How to implement CatBoost for Binary Classification Algorithm in Python.
- How to setup CatBoost hyper-parameters: manual and automatic tuning in Python.
- How to setup RandomSearchCV and GridSearchCV for parameter tuning in Python.
- How to compare Algorithms with Accuracy and Kappa in Python.