One of the major tasks of machine learning algorithms is to construct a fair model from a dataset. The process of generating models from data is called learning or training and the learned model can be called as hypothesis or learner. The learning algorithms which construct a set of classifiers and then classify new data points by taking a choice of their predictions are known as Ensemble methods.In Other words we can say "Ensemble methods is a machine learning technique that combines several base models in order to produce one optimal predictive model."
Why Use Ensemble Methods??
The learning algorithms which output only a single hypothesis tends to suffer from basically three issues. These issues are the statistical problem, the computational problem and the representation problem which can be partly overcome by applying ensemble methods.The learning algorithm which suffers from the statistical problem is said to have high variance. The algorithm which exhibits the computational problem is sometimes described as having computational variance and the learning algorithm which suffers from the representational problem is said to have a high bias. These three fundamental issues can be said as the three important ways in which existing learning algorithms fail. The ensemble methods promise of reducing both the bias and the variance of these three shortcomings of the standard learning algorithm.
Combine Model Predictions Into Ensemble Predictions
The three most popular methods for combining the predictions from different models are:
Bagging. Building multiple models (typically of the same type) from different subsamples of the training dataset.
Boosting. Building multiple models (typically of the same type) each of which learns to fix the prediction errors of a prior model in the chain.
Voting. Building multiple models (typically of differing types) and simple statistics (like calculating the mean) are used to combine predictions
Applications Of Ensemble Methods
1. Ensemble methods can be used as overall diagnostic procedures for a more conventional model building. The larger the difference in fit quality between one of the stronger ensemble methods and a conventional statistical model, the more information that the conventional model is probably missing.
2. Ensemble methods can be used to evaluate the relationships between explanatory variables and the response in conventional statistical models. Predictors or basis functions overlooked in a conventional model may surface with an ensemble approach.
3. With the help of the ensemble method, the selection process could be better captured and the probability of membership in each treatment group estimated with less bias.
4. One could use ensemble methods to implement the covariance adjustments inherent in multiple regression and related procedures. One would “residualized” the response and the predictors of interest with ensemble methods
Why Use Ensemble Methods??
The learning algorithms which output only a single hypothesis tends to suffer from basically three issues. These issues are the statistical problem, the computational problem and the representation problem which can be partly overcome by applying ensemble methods.The learning algorithm which suffers from the statistical problem is said to have high variance. The algorithm which exhibits the computational problem is sometimes described as having computational variance and the learning algorithm which suffers from the representational problem is said to have a high bias. These three fundamental issues can be said as the three important ways in which existing learning algorithms fail. The ensemble methods promise of reducing both the bias and the variance of these three shortcomings of the standard learning algorithm.
Combine Model Predictions Into Ensemble Predictions
The three most popular methods for combining the predictions from different models are:
Bagging. Building multiple models (typically of the same type) from different subsamples of the training dataset.
Boosting. Building multiple models (typically of the same type) each of which learns to fix the prediction errors of a prior model in the chain.
Voting. Building multiple models (typically of differing types) and simple statistics (like calculating the mean) are used to combine predictions
Applications Of Ensemble Methods
1. Ensemble methods can be used as overall diagnostic procedures for a more conventional model building. The larger the difference in fit quality between one of the stronger ensemble methods and a conventional statistical model, the more information that the conventional model is probably missing.
2. Ensemble methods can be used to evaluate the relationships between explanatory variables and the response in conventional statistical models. Predictors or basis functions overlooked in a conventional model may surface with an ensemble approach.
3. With the help of the ensemble method, the selection process could be better captured and the probability of membership in each treatment group estimated with less bias.
4. One could use ensemble methods to implement the covariance adjustments inherent in multiple regression and related procedures. One would “residualized” the response and the predictors of interest with ensemble methods
Conclusion
Although ensemble methods can help you win machine learning competitions by devising sophisticated algorithms and producing results with high accuracy, it is often not preferred in the industries where interpretability is more important. Nonetheless, the effectiveness of these methods are undeniable, and their benefits in appropriate applications can be tremendous. In fields such as healthcare, even the smallest amount of improvement in the accuracy of machine learning algorithms can be something truly valuable.
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