Random Forest is a supervised machine-learning algorithm. A random forest can be used for classification and regression problems. Random Forest is mostly used for classification tasks. It has multiple decision trees for various subsets of the given dataset. We can quickly implement Random Forest in Python using the Sklearn library. You have to follow the given steps to implement the Random Forest classifier.
Step 1: Import the libraries
import seaborn as sns
Step 2: Import the iris dataset
iris_data = sns.load_dataset("iris")
print(iris_data.head())sepal_length sepal_width petal_length petal_width species 0 5.1 3.5 1.4 0.2 setosa 1 4.9 3.0 1.4 0.2 setosa 2 4.7 3.2 1.3 0.2 setosa 3 4.6 3.1 1.5 0.2 setosa 4 5.0 3.6 1.4 0.2 setosa
Step 3: Split the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split X = iris_data.iloc[:, :-1].values y = iris_data.iloc[:, 4].values X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
Step 4: Feature Scaling
from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test)
Step 5: Fitting Random Forest to the Training set using Sklearn
from sklearn.ensemble import RandomForestClassifier model = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0) model.fit(X_train, y_train)
Step 6: Prediction on the Test set
y_pred = model.predict(X_test)
Step 7: Accuracy on the training set and test set
from sklearn.metrics import accuracy_score
print("Accuracy on training set: ", accuracy_score(y_train, model.predict(X_train)))
print("Accuracy on test set", accuracy_score(y_test, y_pred))Accuracy on training set: 1.0 Accuracy on test set 0.9473684210526315
Step 8: Confusion Matrix
from sklearn.metrics import confusion_matrix c_matric = confusion_matrix(y_test, y_pred) print(c_matric)
[[13 0 0] [ 0 15 1] [ 0 1 8]]
Step 9: Visualization of the First Five Decision Trees
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows = 1,ncols = 5,figsize = (10,2), dpi=900)
for index in range(0, 5):
plot_tree(model.estimators_[index], filled=True,ax = axes[index])
axes[index].set_title('Estimator: ' + str(index), fontsize = 11)
plt.show()
One Decision Tree of Random Forest
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 10))
plot_tree(model.estimators_[0], filled=True)
plt.title("One Decision Tree of Random Forest",size=20)
plt.show()
Complete Code:
# Step 1: Import the libraries
import seaborn as sns
# Step 2: Import the iris dataset
iris_data = sns.load_dataset("iris")
# Step 3: Split the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split
X = iris_data.iloc[:, :-1].values
y = iris_data.iloc[:, 4].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.25, random_state = 0)
# Step 4: Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
# Step 5: Fitting Random Forest to the Training set
from sklearn.ensemble import RandomForestClassifier
model = RandomForestClassifier(n_estimators = 10, criterion = 'entropy', random_state = 0)
model.fit(X_train, y_train)
# Step 6: Prediction on Test set
y_pred = model.predict(X_test)
# Step 7: Accuracy on training set and test set
from sklearn.metrics import accuracy_score
print("Accuracy on training set: ", accuracy_score(y_train, model.predict(X_train)))
print("Accuracy on test set", accuracy_score(y_test, y_pred))
# Step 8: Confusion Matrix
from sklearn.metrics import confusion_matrix
c_matric = confusion_matrix(y_test, y_pred)
print(c_matric)Output:
Accuracy on training set: 1.0 Accuracy on test set 0.9473684210526315 [[13 0 0] [ 0 15 1] [ 0 1 8]]
