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## 203.7.9 Boosting Conclusion

When Ensemble doesn’t work? The models have to be independent, we can’t build the same model multiple times and expect the error to reduce. We may have to bring in the independence by choosing subsets of data, or subset of features while building the individual models Ensemble may backfire if …

## 203.7.8 Practice : Boosting

In last post we covered the concepts and theory behind Boosting Algorithms. In this post we will put the concepts into practice and build Boosting models using Scikit Learn in R. LAB: Boosting Rightly categorizing the items based on their detailed feature specifications. More than 100 specifications have been collected. …

## 203.7.7 Boosting

In this post we will cover how boosting work and the type of boosting algorithms. Boosting Boosting is one more famous ensemble method Boosting uses a slightly different techniques to that of bagging. Boosting is a well proven theory that works really well on many of the machine learning problems …

## 203.7.6 Practice : Random Forest

Let’s implement the concept of Random Forest into practice using R. LAB: Random Forest Dataset: /Car Accidents IOT/Train.csv Build a decision tree model to predict the fatality of accident Build a decision tree model on the training data. On the test data, calculate the classification error and accuracy. Build a …

## 203.7.5 The Random Forest

Random Forest Like many trees form a forest, many decision tree model together form a Random Forest model Random forest is a specific case of bagging methodology. Bagging on decision trees is random forest In random forest we induce two types of randomness Firstly, we take the boot strap samples …

## 203.7.4 The Bagging Algorithm

Let’s move forward to the first type of Ensemble Methodology, the Bagging Algorithm. We will cover the concept behind Bagging and implement it using R. The Bagging Algorithm The training dataset D Draw k boot strap sample sets from dataset D For each boot strap sample i Build a classifier …

## 203.7.3 Types of Ensemble Models

In this short post we will just see the types of Ensemble models. Types of Ensemble Models The above example is a very primitive type of ensemble model. There are better and statistically stronger ensemble methods that will yield better results Two most popular ensemble methodologies are Bagging Boosting Bagging …

## 203.7.2 Ensemble Models

In this post we will discuss a bit about Ensemble Models and why they work. Ensemble Models Obtaining a better predictions using multiple models on the same dataset Not every time it is possible to find single best fit model for our data, ensemble model combines multiple models to come …