Recursive Induction of Decision Trees

Recursive induction plays a fundamental role in the construction of decision trees in machine learning. Decision trees are a popular and intuitive algorithm used for both classification and regression tasks. They work by recursively partitioning the input data space into smaller subsets based on the values of input features, ultimately leading to the creation of a tree-like structure where each leaf node represents a decision or prediction.

How it works

• Initial Splitting: The process starts by selecting the best feature to split the data based on certain criteria like Gini impurity, entropy, or mean squared error. The chosen feature is used to split the dataset into subsets, each corresponding to a different value of that feature.
• Recursive Splitting: For each subset created from the previous step, the process is repeated recursively. The algorithm evaluates the best feature for splitting in each subset and continues splitting until a stopping criterion is met. This could be a certain maximum depth of the tree, a minimum number of samples in a leaf node, or other similar conditions.
• Leaf Node Creation: At each step of the recursion, if the stopping criterion is met, a leaf node is created. This leaf node contains the decision or prediction for the subset of data that has reached it.
• Termination: The recursive process terminates when the stopping criteria are satisfied for all nodes. This results in a tree structure where each leaf node contains a prediction, and the path from the root to a leaf node represents the decision-making process based on the values of input features.
• Pruning (Optional): After the tree is constructed, a pruning step can be performed to reduce its complexity and improve generalization. Pruning involves removing branches that do not contribute much to the predictive power of the tree.

Research Papers

• Recursive Induction of Decision Trees

Videos

• Recursive Induction of Decision Trees

        
# -- coding: utf-8 --
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 data 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 decision tree classifier
clf = DecisionTreeClassifier()

# Fit the classifier on the training data
clf.fit(X_train, y_train)

# Make predictions on the testing data
y_pred = clf.predict(X_test)

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