#random forests

Decision Trees are easy to visualize and interpret but they are not very reproducible on future data. This makes them less reliable prediction models and more useful for explanatory data analysis.

Random Forests are coined from Decision Trees but proceeds by growing many trees for model reproducibility. A random sample of observation is selected through a process called bagging. Each of the trees are grown on a different randomly selected sample of bagged data and the remaining unbagged are used for testing the trees.

Random Forest Classifier deals with a categorical response variable/target y, while Random Forest Regressor is used for quantitative targets y. Same goes for Decision Trees too. The accuracy of Classifiers is measured by Confusion matrixes and Accuracy scores but for Regressors, Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root-mean Squares Error (RMSE) are used.

 Summary of Results

Random Forest analysis was performed to examined the importance of ‘incomperperson’ and ‘urbanrate’ as possible contributors to a random forest evaluating the response variable ‘relectricperperson_cat’.

Accuracy of the model built is 82.69% with 6 ‘false-negatives’ and 3 ‘false-positives’. ‘incomperperson’ has almost double the significance of ‘urbanrate’ in the random trees grown. The test on accuracy suggested that the model is higher when growing more than 2 decision trees.

Gradient Boosting Decision Tree

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That paper also shows how you can generate a diverse set of models by various methods (such as forests, gradient boosted decision trees, factorization machines, and logistic regression) and then combine them with stacked ensemble techniques such regularized regression methods, gradient boosting, and hill climbing methods.(More…)

At stage 2 (ensemble stacking), the…

In the world of analytics,modeling is a general term used to refer to the use of data mining (machine learning) methods to develop predictions. If you want to know what ad a particular user is more likely to click on, or which customers are likely to leave you for a competitor, you develop a predictive model.

There are a lot of models to choose from: Regression, Decision Trees, K Nearest…

Step-by-step:

"Getting Started With R: Random Forests"

Random Forest is a machine learning method. This data mining algorithm is based on decision trees, but proceeds by growing many trees. While decision trees are not very reproducible on future data and are proceed by searching for a split on every variable in every node, Random Forests searches for a split on only one variable in a node. The variable that has the largest association with the target among candidate explanatory variables but only among those explanatory variables that have been randomly selected to be tested for that node.

How does it work?

First, a small subset of explanatory variables is selected at random.

Next, the node is split with the best variable among the small number of randomly selected variables.

Then, a new list of eligible explanatory variables is selected on random to split on the next node. This continues until the tree is fully grown, and ideally there is one observation in each terminal mode.

The eligible variables set will be quite different from node to node.

However, important variables will eventually make it into the tree. Their relative success in predicting the target variable will begin to get them larger and larger numbers of "votes" in their favor.

The growing of each tree in a random forest is not only based on subsets of explanatory variables at each node, but also based on a random subset of the sample for each tree in the forest.