Scikit-Learn RandomForestClassifier Model

RandomForestClassifier is an ensemble classification algorithm that constructs multiple decision trees during training and outputs the mode of the classes predicted by individual trees.

Key hyperparameters include n_estimators (number of trees), max_depth (maximum depth of each tree), and min_samples_split (minimum number of samples required to split a node).

This algorithm is suitable for both binary and multi-class classification problems.

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score

# generate binary classification dataset
X, y = make_classification(n_samples=100, n_features=5, n_classes=2, 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 = RandomForestClassifier()

# fit model
model.fit(X_train, y_train)

# evaluate model
yhat = model.predict(X_test)
acc = accuracy_score(y_test, yhat)
print('Accuracy: %.3f' % acc)

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

Running the example gives an output like:

Accuracy: 0.950
Predicted: 0

1. Generate a synthetic binary classification dataset using the make_classification() function, specifying the number of samples, features, and classes. Use a fixed random seed for reproducibility. Split the dataset into training and testing sets using train_test_split().

2. Instantiate a RandomForestClassifier with default hyperparameters. Fit the model on the training data using the fit() method.

3. Evaluate the model by making predictions on the test set and calculating the accuracy score using accuracy_score.

4. Make a prediction on a new data sample using the predict() method.