1. Introduction

In this tutorial, our primary goal is to introduce ensemble methods in machine learning and demonstrate how to create an ensemble of different machine learning models for improved prediction accuracy. Ensemble methods are powerful tools that combine decisions from multiple models to improve the overall performance.

By the end of the tutorial, you will have a solid understanding of how ensemble methods work, how to create your own ensemble models, and how to use them for prediction tasks.

The prerequisites for this tutorial are:
- Basic understanding of Python programming
- Familiarity with fundamental concepts of machine learning
- Basic knowledge of Scikit-Learn library in Python

2. Step-by-Step Guide

An ensemble method in machine learning constructs a set of classifiers and then classifies new data points by taking a (weighted) vote of their predictions. The original ensemble method is Bayesian averaging, but more recent algorithms include error-correcting output coding, bagging, and boosting.

Bagging stands for bootstrap aggregation. It combines multiple learners in a way to reduce the variance of estimates. For example, Random Forest is a type of bagging algorithm.

Boosting is a sequential technique which works on the principle of an ensemble. It combines a set of weak learners and delivers improved prediction accuracy.

Now let's look at an example of creating an ensemble model using Scikit-Learn library in Python.

3. Code Examples

Let's say we have a classification problem and we will use the famous Iris dataset for this example.

First, let's import required libraries.

from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

Next, we load the dataset and split it into training and testing sets.

iris = load_iris()
X = iris.data[:, :4]
y = iris.target

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

Now, we will create different machine learning models.

log_clf = LogisticRegression(solver="lbfgs", random_state=42)
rnd_clf = RandomForestClassifier(n_estimators=100, random_state=42)
svm_clf = SVC(gamma="scale", random_state=42)

Next, we will create an ensemble of models.

voting_clf = VotingClassifier(
    estimators=[('lr', log_clf), ('rf', rnd_clf), ('svc', svm_clf)],
    voting='hard')

voting_clf.fit(X_train, y_train)

Let's evaluate each model’s accuracy on the test set.

for clf in (log_clf, rnd_clf, svm_clf, voting_clf):
    clf.fit(X_train, y_train)
    y_pred = clf.predict(X_test)
    print(clf.__class__.__name__, accuracy_score(y_test, y_pred))

You should see the accuracy of each model printed out, and typically, the voting classifier outperforms all the individual classifiers.

4. Summary

In this tutorial, we discussed ensemble methods in machine learning, what they are and why they are used. We also created an ensemble of different machine learning models and used it on the Iris dataset for a prediction task.

Next steps for learning could be exploring different ensemble methods like Stacking and Bagging. You can also study how to tune these models for better performance.

5. Practice Exercises

1. Try creating an ensemble of three different regression models on the Boston Housing dataset.

2. Use the ensemble model to predict the house prices and compare the result with the actual values.

3. Experiment with different ensemble methods like Bagging and Boosting on the MNIST dataset.

Remember, practice is key when it comes to mastering machine learning concepts. Happy coding!