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Scikit-Learn ExtraTreeRegressor Model

ExtraTreeRegressor is a type of decision tree used for regression tasks, which differs from standard decision trees by selecting splits at random.

Key hyperparameters include max_depth (maximum depth of the tree), min_samples_split (minimum number of samples required to split an internal node), and min_samples_leaf (minimum number of samples required to be at a leaf node).

This algorithm is suitable for regression tasks where the goal is to predict a continuous target variable.

from sklearn.datasets import make_regression
from sklearn.model_selection import train_test_split
from sklearn.tree import ExtraTreeRegressor
from sklearn.metrics import mean_squared_error

# generate 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 = ExtraTreeRegressor()

# fit model
model.fit(X_train, y_train)

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

# make a prediction
row = [[-1.10325445, -0.49821356, -0.05962247, -0.89224592, -0.70158632]]
yhat = model.predict(row)
print('Predicted: %.3f' % yhat[0])

Running the example gives an output like:

MSE: 2120.439
Predicted: -39.917

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), noise (noise), and a fixed random seed (random_state) for reproducibility. The dataset is split into training and test sets using train_test_split().

  2. Next, an ExtraTreeRegressor model is instantiated with default hyperparameters. 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 quickly set up and use an ExtraTreeRegressor model for regression tasks, showcasing the simplicity and effectiveness of this algorithm in scikit-learn. The model can be trained on the data and used to make predictions on new samples, making it applicable to various regression problems.



See Also