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- How they work, implementation in Python and Java.
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How Decision Trees Work
Decision trees are a popular supervised learning algorithm used for classification and regression tasks. They work by recursively splitting the dataset into subsets based on feature values, forming a tree-like structure where each node represents a decision point and each branch represents an outcome.
Implementation in Python (Scikit-learn)
Step 1: Import Libraries
pythonCopier le codefrom sklearn.tree import DecisionTreeClassifierfrom sklearn.model_selection import train_test_split
Step 2: Prepare Data and Split into Training/Testing Sets
pythonCopier le codeX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
Step 3: Train the Model
pythonCopier le codemodel = DecisionTreeClassifier()model.fit(X_train, y_train)
Step 4: Make Predictions
pythonCopier le codepredictions = model.predict(X_test)
Implementation in Java (Weka)
Step 1: Load the Dataset
javaCopier le codeInstances data = new Instances(new BufferedReader(new FileReader(“data.arff”)));data.setClassIndex(data.numAttributes() – 1);
Step 2: Build the Decision Tree Model
javaCopier le codeJ48 tree = new J48(); // J48 is Weka’s implementation of C4.5 decision treetree.buildClassifier(data);
Step 3: Evaluate the Model
javaCopier le codeEvaluation eval = new Evaluation(data);eval.crossValidateModel(tree, data, 10, new Random(1));