Ensemble Machine Learning in Python : Adaboost, XGBoost

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.

Reducing Variance & reducing high bias error are such important task while designing the machine learning system and Ensemble learning is the solution behind that.

Why ensemble learning :

Build model with low variance and low bias.