Hyperparameter Tuning in Decision Trees

In this section, we will discuss the key hyperparameters used in the decision tree algorithm. Decision tree models are prone to overfitting, making hyperparameter tuning crucial for improving their performance. This discussion is largely inspired by the scikit-learn documentation. For a complete list of hyperparameters, you can refer to this link. Below are some of the most important hyperparameters:

• max_depth

  One of the most important hyperparameters, max_depth defines the maximum depth of the tree. A greater depth results in more partitions in the data, increasing the risk of overfitting. On the other hand, a tree with very low depth may underfit the data. Therefore, choosing an appropriate depth is essential to ensure a well-balanced model.
  Practical Tip: Start your experiments with max_depth=3 and gradually increase the depth to find the optimal value. More practical advice can be found in this guide.

• max_features

  This hyperparameter determines how many features will be considered when searching for the best feature and threshold at each node of the tree. A subset of features is randomly selected from all available features. A commonly used value for this parameter is sqrt(n_features).
  For example, if there are 16 features in the dataset, only 4 randomly chosen features will be used to find the best feature and threshold at each node. More details can be found in the scikit-learn documentation.
  This parameter helps speed up training and prevents overfitting by reducing the risk of the model memorizing training data.

• min_samples_split

  This parameter defines the minimum number of data points a node must have before it can be split into child nodes. If a node contains fewer data points than the specified threshold, it will not be split further. This helps control the tree’s depth and prevents overfitting.

• min_samples_leaf

  If a node is split, each child node must contain at least this many data points. If a split would result in a child node containing fewer data points than this threshold, the split is discarded. This parameter helps regulate tree depth and ensures that leaf nodes contain enough samples for meaningful predictions.

For more practical advice on setting the right hyperparameter values for your experiments, you can refer to the scikit-learn documentation.