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Decision Tree in Machine Learning Explained
Decision tree is a popular and versatile algorithm in machine learning that can be used for both classification and regression tasks. It creates a model that predicts the value of a target variable based on several input variables. In this article, we will explain how decision tree works and provide a Python code example to implement it.
How Decision Tree Works
A decision tree works by recursively partitioning the input space into smaller regions based on the values of the input variables. It starts with the entire dataset and identifies the best split that separates the data into two groups that are as homogeneous as possible with respect to the target variable. This process is repeated for each resulting subset until a stopping criterion is met.
Python Code Example
Below is a Python code example that demonstrates how to build and train a decision tree model using the popular scikit-learn library:
“`python
from sklearn.datasets import load_iris
from sklearn.tree import DecisionTreeClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
# Load the Iris dataset
iris = load_iris()
X = iris.data
y = 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)
# Initialize the decision tree classifier
clf = DecisionTreeClassifier()
# Train the model
clf.fit(X_train, y_train)
# Make predictions on the testing set
y_pred = clf.predict(X_test)
# Calculate the accuracy of the model
accuracy = accuracy_score(y_test, y_pred)
print(“Accuracy: “, accuracy)
“`
Conclusion
Decision tree is a powerful and interpretable algorithm that is widely used in machine learning. By understanding how decision tree works and applying it in Python code, you can build predictive models for a variety of tasks. We hope this article has provided you with a solid understanding of decision tree in machine learning.
