Decision Trees Algorithms

Source: Tee in green wheat field from Johann Siemens (https://unsplash.com/photos/EPy0gBJzzZ)

Decision tree learning is a commonly used algorithm in the field of machine learning, and one of the most popular libraries for implementing decision trees is scikit-learn, or sklearn for short.

There are several reasons why I like decision trees:

1. Intuitive: Decision trees are easy to understand and interpret, even for people with little background in statistics or machine learning. The tree structure makes it easy to visualize how the algorithm is making its decisions, and the rules it generates can be easily explained to non-technical stakeholders as shown in the below graph:

Source: Explainable Software Analytics — Scientific Figure on ResearchGate. Available from: https://www.researchgate.net/figure/Prediction-accuracy-versus-explainability_fig1_322929376 [accessed 14 Jan, 2023]

2. Flexible: Decision trees can be used for both classification and regression tasks and can handle both continuous and categorical variables. They can also be easily adapted to handle multi-output problems.

3. Handling missing values: Decision trees can handle missing values without the need for imputation.

4. Non-linear relationship: Decision trees can model non-linear relationships between variables, making them well-suited for a wide range of problems.

5. Scalable: Decision trees can be scaled to handle large datasets and can be parallelized to speed up training and prediction.

6.Robust: Decision trees are not sensitive to the scale of the input variables. They can also handle irrelevant or redundant features.

7. Efficient: Decision trees are efficient and fast to train and predict, making them well-suited for real-time applications.

8. Ensemble methods: Decision trees can be combined with other algorithms to create ensemble methods such as random forests and gradient boosting, which are known to be very powerful methods.

All these advantages make decision tree a versatile and popular algorithm among data scientists and machine learning practitioners. It is widely used in industry and research, and it’s a great algorithm to start with for those who are new to machine learning.

When training a decision tree model, there are a number of hyperparameters that can be adjusted to fine-tune the performance of the model. These hyperparameters include:

1. Maximum depth: This hyperparameter controls the maximum depth of the tree, which is the number of levels from the root to the leaf node. A deeper tree can lead to overfitting, while a shallower tree can lead to underfitting.
2. Minimum samples per leaf: This hyperparameter controls the minimum number of samples that are allowed in a leaf node. A higher value will lead to a simpler tree with less overfitting, while a lower value will lead to a more complex tree with more overfitting.
3. Minimum samples per split: This hyperparameter controls the minimum number of samples that are allowed in a split. A higher value will lead to a simpler tree with less overfitting, while a lower value will lead to a more complex tree with more overfitting.
4. Maximum features: This hyperparameter controls the maximum number of features that are used to split at each node. A higher value will lead to a more complex tree with more overfitting, while a lower value will lead to a simpler tree with less overfitting.
5. Criterion: The criterion used to measure the quality of a split. The most common criteria are Gini impurity and entropy.
6. Splitter: The strategy used to choose the split at each node. The most common strategies are the best-first and random.

In general, when training a decision tree model, it is important to carefully tune the hyperparameters to achieve the best performance. This can be done using techniques such as grid search or random search, which involve training the model with different combinations of hyperparameters and evaluating the performance on a validation set. It is also important to evaluate the model performance on a test set to get a more realistic performance of the model.

In addition to these hyperparameters, there are other methods that can be used to avoid overfitting such as pruning, which removes the branches of the tree that do not contribute to the accuracy of the model. Another technique is ensemble methods that combine multiple decision trees to make a final prediction.

It’s worth mentioning that the choice of hyperparameters and the method used to tune them, can greatly affect the performance and generalization of the model.