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Ensemble Machine Learning in Python : Adaboost, XGBoost

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Ensemble Machine Learning in Python : Adaboost, XGBoost

Ensemble Machine Learning in Python : Adaboost, XGBoost

Bagging ensembles methods are Random Forest and Extra Trees. Boosting ... AdaBoost (Adaptive Boosting) NEW
by Ankit Mistry, Data Science & Machine Learning Academy by Ankit Mistry
What you'll learn

  • Machine learning concept and bias variance error.
  • Concept behind Ensemble learning and Different types of ensemble learning
  • Apply voting classifier and voting regressor with Scikit-learn API
  • Understand and implement bagging ensemble learning method
  • Apply special bagging ensemble technique Random forest on credit card Dataset.
  • Learn adaboost and XGBoost ensemble technique
  • Understand and implement Model stacking technique

Description
Let's say you want to take one of the very important decision in your life, it will be a choosing your career or choosing your life partner.

Do you think that you can depend on a just one person advice. Advice from the one person can be highly biased also. The best way you can go ahead by asking and taking guidance from multiple people which reduce the bias.

Same thing apply on machine learning world also while predicting some class or predicting any continuous value for regression problem, why you should rely on a one model only. support vector machine, neural network, decision tree, random forest logistic regression, genetic algorithm.

This type of many algorithms are available. Why don't we use the capability of many algorithm for prediction. So using those power of multiple algorithm for the prediction is called as  ENSEMBLE LEARNING.

So welcome to my course on and Ensemble  Machine learning with Python.

One of the most useful technique in machine learning to balance bias and variance.

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