Ensemble Learning
Ensemble Learning Techniques in Machine Learning, Machine learning models suffer bias and/or variance. Bias is the difference between the predicted value and actual value by the model. Bias is introduced when the model doesn’t consider the variation of data and creates a simple model. The simple model doesn’t follow the patterns of data, and hence the model gives errors in predicting training as well as testing data i.e. the model with high bias and high variance. When the model follows even random quirks of data, as pattern of data, then the model might do very well on training dataset i.e. it gives low bias, but it fails on test data and gives high variance. Therefore, to improve the accuracy (estimate) of the model, ensemble learning methods are developed. Ensemble is a machine learning concept, in which several models are trained using machine learning algorithms. It combines low performing classifiers (also called as weak learners or base learner) and combine individual model prediction for the final prediction. On the basis of type of base learners, ensemble methods can be categorized as homogeneous and heterogeneous ensemble methods. If base learners are same, then it is a homogeneous ensemble method. If base learners are different then it is a heterogeneous ensemble method.
Applications of Ensemble Learning? Let's Understand!
● Disease Detection
● Remote Sensing
● Fraud Detection
● Speech emotion recognition
About Ensemble Learning: Let's See!
●Tensorflow
●sci-kit learn
●Py torch
● Keras
●NumPy
●Pandas
●Matplotlib
●Theano
●Scipy
●Plotly
●Statsmodels
