The classification task is to predict whether a breast mass is malignant (cancerous) or benign (non-cancerous) based on the extracted features. This task is crucial in medical diagnosis, as early detection of malignant tumors can significantly improve patient outcomes. A decision tree is a reasonable model to try for this data because: Decision trees are interpretable: They provide clear insights into the decision-making process, which is essential in medical applications where understanding the reasoning behind predictions is critical. Decision trees can handle both numerical and categorical data: The dataset contains numerical features, making decision trees a suitable choice. Decision trees are robust to irrelevant features: They can automatically select the most relevant features for making decisions, which is beneficial in datasets with many features like this one.

key features

1.Import necessary libraries:

sklearn.model_selection sklearn.tree sklearn.metrics

2.Split data:

train_test_split()

3.Create and train the model:

DecisionTreeClassifier() clf.fit(X_train, y_train)

4.Evaluate model performance:

accuracy_score() classification_report()

5.Hyperparameter tuning:

max_depth, min_samples_split, min_samples_leaf