DecisionTreeClassifier()

DecisionTreeClassifier() is a machine learning algorithm used for classification problems. It creates a decision tree model based on the training data, where each node in the tree represents a decision based on a feature value, and each leaf node represents a class label. The algorithm works by recursively splitting the data into subsets based on the values of the features, until it reaches a stopping criterion, such as a maximum depth or a minimum number of samples per leaf. The model can be used to predict the class label of new data points by traversing the decision tree from the root to a leaf node that corresponds to the predicted class label. The algorithm can handle both categorical and numerical features, and can also handle missing values. However, decision trees are prone to overfitting, especially if the tree is too deep or the data is noisy, so various techniques such as pruning, ensemble methods, or regularization can be used to improve the model's performance.