Ensemble Learning Eradicates Challenges of ML Models

Ensemble Learning ensures training multiple machine learning models at the same time with their combining outputs.

Machine learning models, a subset of AI require voluminous data to train the datasets. Though the data that gets collected every day by organizations is huge, data becomes a persistent problem for machine learning models to train the datasets. The data collected is either too less or too huge for training the datasets, which ultimately influences the decision-making capability of the models. An inaccurate amount of data results in undesired outcomes. Moreover, the entire process becomes disrupted. Organizations are rendered to face such challenges often. To eradicate such problems ensemble learning comes to the rescue.

Ensemble Learning is the technique of machine learning, where diverse multiple machine learning models or classifiers are trained at the same time with their combining outputs. The different models are used as a base to create one predictive model. Moreover owing to the diverse ML models involved, it ensures enhanced reliability, improved stability and accurate predictions. They are strategically generated and combined to solve the computational process, which is otherwise not possible using a single model. Since different machine learning models are trained on a varied data population, using different modeling techniques, the result ensures accuracy. Moreover, the ensemble learning focuses on improving the classification prediction, functions of a model and reduces the instances of making a bad decision.

These Ensemble learning models are trained by using simple and advanced techniques.

The concept behind Ensemble Learning Models

The training of ensemble learning models is based on the psychological model of training humans for teamwork. For example, in a competition, an individual might not know the answer for all the questions asked to him/her. The individual will rely on guessing the answer, which will make the individual prone to failure. However in a team, while one team member doesn't know the answer of any particular topic, the rest of his/her team members might be aware of the answers for the said question. Let's say if three members of the team are confident to conclude a similar answer for the question and three are making varied assumptions regarding the answer, the probability of giving the wrong answer gets distributed. The team will pick the confident answer ensuring reliability. Furthermore, the wrong guesses are distributed to the individuals having all the wrong answers, thus cancelling each other out. And the correct guess will be clustered around the correct answer.

Similarly, as multiple machine learning models are trained in ensemble learning technique, the incidents of making errors get distributed. For example, if a machine learning model has insufficient data to be trained on, this issue will be negated by the models that have sufficient or large amount of data. This implies less room for making mistakes with improved accuracy. Since all models are prone to make different errors to a different extent, the errors get distributed across the thread of models, instead of getting clustered to a particular one. Instead of data clustering over the single model, the data also gets distributed across the thread of machine learning models, thus reducing the incidents of errors and improving accuracy.

Diverse Decision Making

For training the dataset, it is imperative to have a good machine learning model. The performance of a machine learning model equally contributes to the positive outcomes for the training a dataset. A poor-performance dataset training model proves highly erroneous for the entire process.

Using an ensemble of such models, and combining their outputs reduces the incidents of choosing a particularly poor model. Moreover, it induces diverse decisions amongst different models.

Addressing the Data Issues

Like mentioned earlier, the correct amount of data becomes paramount while training any model. On the hindsight, if a model is trained on huge data, then this data is strategically divided into subsets, which is used to train separate models. If the data is too small then methods like bootstrapping can be used to train different models.

Data Fusion

Many applications that stimulates automated decision making, different sources which provides data also gives complimentary information. This is known as data fusion, which enhances the accuracy of decision making. But this complimentary information cannot be used to train a particular ML model. In such cases, ensemble learning is used, where each ML model is trained in a particular set of information. The decision made by different ML/model is then utilized to combine a particular outcome.

Confidence Estimation

Ensemble learning promotes confidence in the decision made by the system. If the vast majority of ML-models display similar decisions, it implies high confidence in the decision. Though the high confidence doesn't guarantee the correct decision, it estimates the posterior probabilities of the classification decisions.