Which of the following methods enhances prediction by combining objectives from multiple decision trees?

The method that enhances prediction by combining objectives from multiple decision trees is Random Forest. This approach operates by aggregating the predictions from many decision trees to improve accuracy and control overfitting, leading to a more reliable outcome.

In Random Forest, each tree is trained on a randomly sampled subset of the data, and features are also chosen randomly when splitting nodes in the trees. This randomness helps create a diverse ensemble of trees, making the overall model more robust to variations and reducing the likelihood of capturing noise from the training data. The final prediction is typically made through a voting mechanism (for classification) or averaging (for regression) across all the individual trees, which helps to smooth out individual model biases and errors.

In contrast, the other methods mentioned do not utilize the ensemble learning strategy intrinsic to Random Forest. Support Vector Machines focus on creating boundaries for classification rather than combining multiple models, while Lasso Regression and Simple Linear Regression are single-model approaches that use linear relationships between variables without creating ensemble structures.