Lab 8

 Import matplotlib.pyplot as plt

From sklearn import datasets

From sklearn.model_selection import train_test_split

From mlxtend.plotting import plot_decision_regions

From sklearn.metrics import accuracy_score

From sklearn.ensemble import RandomForestClassifier

# Load IRIS data set

Iris = datasets.load_iris()

X = iris.data[:, 2:]

Y = iris.target

# Create training/ test data split

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1, stratify=y)

# Create an instance of Random Forest Classifier

Forest = RandomForestClassifier(criterion=’gini’,

                                 N_estimators=5,

                                 Random_state=1,

                                 N_jobs=2)

# Fit the model

Forest.fit(X_train, y_train)

# Measure model performance

Y_pred = forest.predict(X_test)

Print(‘Accuracy: %.3f’ % accuracy_score(y_test, y_pred))

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