Deploying Prediction Models

  Supervised Classification | hyperparameter optimization and voting ensemble Using the Titanic dataset, in this exercise, we will perform GridSearchCV on five different classification algorithms to fit the models using the best-performing hyperparameters. Afterwards, we will use an ensemble voting method, a technique in machine learning where multiple individual models are combined to make predictions.  The idea behind ensemble methods is that by combining the predictions of multiple models, overall performance can be better than that of individual models. In this case we will use: Soft Voting: In this approach, each model provides a probability or confidence score for each class, and the scores are averaged or weighted to make the final decision. This is particularly useful when individual models can produce probability estimates. Hard Voting: Each model in the ensemble predicts a class, and the class with the most votes is chosen as the final prediction. #import necessary...