The Decision Tree algorithm has a major disadvantage in that it causes over-fitting. In addition, random forest is robust against outliers and collinearity. We simply estimate the desired Regression Tree on many bootstrap samples (re-sample the data many times with replacement and re-estimate the model) and make the final prediction as the average of the predictions across the trees. The trees in random forests are run in parallel. The next step would be to split data into train and test as below. Random Forest Regression is one of the fastest machine learning algorithms giving accurate predictions for regression problems. Step 3: Choose the number Ntree of trees you want to build and repeat STEPS 1 & 2. Random Forest . Step 1: Pick at random k data points from the training set. The Steps Required to Perform Random Forest Regression. In this lesson, we are going to learn about Random Forests that are essentially a collection of many decision trees. #3 Fitting the Random Forest Regression Model to the dataset # Create RF regressor here from sklearn.ensemble import RandomForestRegressor #Put 10 for the n_estimators argument. We have to use values of x to predict y. Random forest is a bagging technique and not a boosting technique. Random forest is a Supervised Learning algorithm which uses ensemble learning method for classification and regression. Lastly, keep in mind that random forest can be used for regression and classification trees. We also mentioned the downside of using Decision trees is their tendency to overfit as they are highly sensitive to small changes in data. The function in a Linear Regression can easily be written as y=mx + c while a function in a complex Random Forest Regression seems like a black box that can’t easily be represented as a function. Random Forest Regression Data Load. Note that the above data has a feature called x and a label called y. Step 2: Build the decision Tree associated with this K data point. Random Forest Regression works on a principle that says a number of weakly predicted estimators when combined together form a strong prediction and strong estimation. Random Forest. In our example, we will use the “Participation” dataset from the “Ecdat” package. Random Forest Regression Structure. As mentioned before, the Random Forest solves the instability problem using bagging. The term ‘Random’ is due to the fact that this algorithm is a forest of ‘Randomly created Decision Trees’. We will create a random forest regression tree to predict income of people. In the previous lesson, we discussed Decision Trees and its implementation in Python. This problem can be limited by implementing the Random Forest Regression in place of the Decision Tree Regression.