Scikit-Learn RandomForestRegressor Model

RandomForestRegressor is a powerful ensemble learning algorithm that combines multiple decision trees to improve regression accuracy.

Key hyperparameters include n_estimators (number of trees in the forest), max_depth (maximum depth of the trees), and min_samples_split (minimum number of samples required to split an internal node).

This algorithm is suitable for regression problems where the goal is to predict a continuous output.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error

# generate a synthetic regression dataset
X, y = make_regression(n_samples=100, n_features=5, noise=0.1, random_state=1)

# split into train and test sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1)

# create model
model = RandomForestRegressor(n_estimators=100, max_depth=10, random_state=1)

# fit model
model.fit(X_train, y_train)

# evaluate model
yhat = model.predict(X_test)
mse = mean_squared_error(y_test, yhat)
print('Mean Squared Error: %.3f' % mse)

# make a prediction
row = [[-0.67888615, -0.09470897, 1.49138963, -0.638902, -0.44398196]]
yhat = model.predict(row)
print('Predicted: %.3f' % yhat[0])

Running the example gives an output like:

Mean Squared Error: 1078.389
Predicted: -17.048

The steps are as follows:

1. First, a synthetic regression dataset is generated using the make_regression() function. This creates a dataset with a specified number of samples (n_samples), features (n_features), and a fixed random seed (random_state) for reproducibility. The dataset is split into training and test sets using train_test_split().

2. Next, a RandomForestRegressor model is instantiated with 100 trees (n_estimators) and a maximum depth of 10 (max_depth). The model is then fit on the training data using the fit() method.

3. The performance of the model is evaluated by comparing the predictions (yhat) to the actual values (y_test) using the mean squared error metric.

4. A single prediction can be made by passing a new data sample to the predict() method.

This example demonstrates how to set up and use a RandomForestRegressor model for regression tasks, showcasing the flexibility and effectiveness of this algorithm in scikit-learn.

The model can handle the training data without the need for scaling or normalization. Once trained, it can be used to make predictions on new data, making it a practical choice for real-world regression problems.