Random Forest Regression

The Random Forest is one of the most effective machine learning models for predictive analytics, making it an industrial workhorse for machine learning.


The random forest model is a type of additive model that makes predictions by combining decisions from a sequence of base models. More formally we can write this class of models as:

where the final model is the sum of simple base models . Here, each base classifier is a simple decision tree. This broad technique of using multiple models to obtain better predictive performance is called model ensembling. In random forests, all the base models are constructed independently using a different subsample of the data.

Introductory Example
import graphlab as gl

# Load the data
data =  gl.SFrame.read_csv('https://static.turi.com/datasets/xgboost/mushroom.csv')

# Label 'p' is edible
data['label'] = data['label'] == 'p'

# Make a train-test split
train_data, test_data = data.random_split(0.8)

# Create a model.
model = gl.random_forest_regression.create(train_data, target='label',
                                           max_depth =  3)

# Save predictions to an SArray
predictions = model.predict(test_data)

# Evaluate the model and save the results into a dictionary
results = model.evaluate(test_data)

We can visualize the models using

model.show(view="Tree", tree_id=0)
model.show(view="Tree", tree_id=1)

Alt text Alt text

Tuning hyperparameters

The Gradient oosted Trees model has many tuning parameters. Here we provide a simple guideline for tuning the model.

  • num_trees Controls the number of trees in the final model. Usually the more trees, the higher accuracy. However, both the training and prediction time also grows linearly in the number of trees.

  • max_depth Restricts the depth of each individual tree to prevent overfitting.

  • step_size Also called shrinkage, appeared as the in the equations in the background section. It works similar to the learning rate of the gradient descent procedure: smaller value will take more iterations to reach the same level of training error of a larger step size. So there is a trade off between step_size and number of iterations.

  • min_child_weight One of the pruning criteria for decision tree construction. In classification problem, this corresponds to the minimum observations required at a leaf node. Larger value produces simpler trees.

  • min_loss_reduction Another pruning criteria for decision tree construction. This restricts the reduction of loss function for a node split. Larger value produces simpler trees.

  • row_subsample Use only a fraction of data at each iteration. This is similar to the mini-batch stochastic gradient descent which not only reduce the computation cost of each iteration, but may also produce more robust model.

  • column_subsample Use only a subset of the columns to use at each iteration.

In general, you can choose num_trees to be as large as your computation budget permits. You can then set min_child_weight to be a reasonable value around (#instances/1000), and tune max_depth. When you have more training instances, you can set max_depth to a higher value. When you find a large gap between the training loss and validation loss, a sign of overfitting, you may want to reduce depth, and increase min_child_weight.

Why chose random forests?

Different kinds of models have different advantages. The random forest model is very good at handling tabular data with numerical features, or categorical features with fewer than hundreds of categories. Unlike linear models, random forests are able to capture non-linear interaction between the features and the target.

One important note is that tree based models are not designed to work with very sparse features. When dealing with sparse input data (e.g. categorical features with large dimension), we can either pre-process the sparse features to generate numerical statistics, or switch to a linear model, which is better suited for such scenarios.

Advanced Features

Refer to the earlier chapters for the following features: