یادگیری الگوریتم‌های یادگیری گروهی در یادگیری ماشینی (با مثال‌های پایتون)

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

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create individual classifiers
logistic_classifier = LogisticRegression(random_state=42)
tree_classifier = DecisionTreeClassifier(random_state=42)
svm_classifier = SVC(random_state=42)

# Create a VotingClassifier with majority rule
voting_classifier = VotingClassifier(
    estimators=[
        ('logistic', logistic_classifier), 
        ('tree', tree_classifier), 
        ('svm', svm_classifier)],
    voting='hard'  # 'hard' for majority vote, 'soft' for weighted vote
)

# Fit the ensemble classifier to the training data
voting_classifier.fit(X_train, y_train)

# Make predictions on the test data
y_pred = voting_classifier.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import BaggingClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a base classifier (Decision Tree)
base_classifier = DecisionTreeClassifier(random_state=42)

# Create a BaggingClassifier
bagging_classifier = BaggingClassifier(
    base_estimator=base_classifier,  # Base classifier to be used
    n_estimators=10,  # Number of base classifiers (decision trees)
    random_state=42,
)

# Fit the bagging classifier to the training data
bagging_classifier.fit(X_train, y_train)

# Make predictions on the test data
y_pred = bagging_classifier.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")

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

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a Random Forest Classifier
random_forest = RandomForestClassifier(n_estimators=100, random_state=42)

# Fit the model to the training data
random_forest.fit(X_train, y_train)

# Make predictions on the test data
y_pred = random_forest.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import AdaBoostClassifier
from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create a base classifier (e.g., Decision Tree)
base_classifier = DecisionTreeClassifier(max_depth=1)

# Create an AdaBoost Classifier
adaboost_classifier = AdaBoostClassifier(
    base_estimator=base_classifier,
    n_estimators=50,  # Number of weak learners (you can adjust this)
    random_state=42
)

# Fit the model to the training data
adaboost_classifier.fit(X_train, y_train)

# Make predictions on the test data
y_pred = adaboost_classifier.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Accuracy: {accuracy * 100:.2f}%")

from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, StackingClassifier
from sklearn.metrics import accuracy_score

# Load the Iris dataset
iris = load_iris()
X, y = iris.data, iris.target

# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Create individual classifiers
estimators = [
    ('logistic', LogisticRegression(random_state=42)),
    ('tree', DecisionTreeClassifier(random_state=42)),
    ('rf', RandomForestClassifier(random_state=42))
]

# Create the StackingClassifier
stacking_classifier = StackingClassifier(
    estimators=estimators,
    final_estimator=LogisticRegression()  # You can choose any final estimator
)

# Fit the StackingClassifier to the training data
stacking_classifier.fit(X_train, y_train)

# Make predictions on the test data
y_pred = stacking_classifier.predict(X_test)

# Calculate accuracy
accuracy = accuracy_score(y_test, y_pred)
print(f"Stacking Classifier Accuracy: {accuracy * 100:.2f}%")