Decision Trees In ML Complete Guide [How To Tutorial, Examples, 5 Types & Alternatives]

What are Decision Trees?

Decision trees are versatile and intuitive machine learning models for classification and regression tasks. It represents decisions and their possible consequences, including chance event outcomes, resource costs, and utility. The structure of a decision tree resembles a flowchart, where each internal node denotes a test on an attribute, each branch represents the outcome of the test, and each leaf node represents a class label or a continuous value.

Components of a Decision Tree:

1. Root Node: The topmost node in a decision tree. It represents the entire dataset and is split into two or more homogeneous sets.
2. Internal Nodes: Nodes within the tree that represent decision points. Each internal node corresponds to an attribute test.
3. Leaf Nodes (Terminal Nodes): Nodes at the end of the branches, which provide the outcome of the decision path. For classification tasks, they represent class labels. For regression, tasks represent continuous values.
4. Branches: Arrows connecting nodes, representing the outcome of a test and leading to the next node or leaf.

Example of a Simple Decision Trees

Suppose we have a dataset about whether a person will play tennis based on weather conditions. The dataset includes the following features: Outlook, Temperature, Humidity, and Wind. The target variable is PlayTennis.

Here is a small sample of the dataset:

Decision Tree Construction

We’ll create a simple decision tree based on the given dataset. Here’s an example of what the decision tree might look like:

1. Root Node: The root node splits the dataset based on the Outlook feature, as this feature might provide the highest information gain.
2. Outlook = Sunny: If the outlook is sunny, we further split based on the Humidity:
   • Humidity = High: If the humidity is high, the outcome is No (PlayTennis = No).
   • Humidity = Normal: If the humidity is normal, the outcome is Yes (PlayTennis = Yes).
3. Outlook = Overcast: If the outlook is overcast, the outcome is Yes (PlayTennis = Yes).
4. Outlook = Rainy: If the outlook is rainy, we further split based on the Wind:
   • Wind = Weak: If the wind is weak, the outcome is Yes (PlayTennis = Yes).
   • Wind = Strong: If the wind is strong, the outcome is No (PlayTennis = No).

This decision tree provides a visual representation of how decisions are made based on the feature values. Each path from the root to a leaf represents a series of decisions leading to a final prediction.

How Do Decision Trees Work?

Decision trees function by recursively splitting a dataset into smaller and smaller subsets based on specific criteria to make the most informative decisions at each step. This process continues until the algorithm determines that further splits would not add significant value or a predefined stopping criterion is met. Here’s a detailed look at how this process works:

1. The Decision-Making Process

The decision-making process in a decision tree involves evaluating each attribute (feature) and deciding the best way to split the data based on that attribute. This is done to maximize the separation of the data points into distinct classes or values.

2. Splitting Criteria

Splitting the data is a crucial part of building a decision tree. Various criteria can be used to determine the best split:

• Gini Impurity: Measures the frequency at which a randomly chosen element would be incorrectly classified. A lower Gini impurity indicates a purer node.
• Entropy (Information Gain): Measures the unpredictability or disorder. Information gain is used to decide the optimal split by reducing entropy the most.
• Variance Reduction: Used for regression trees to measure the reduction in variance for the dependent variable.

3. Example

Let’s consider an example decision tree for a simple dataset with features like “Weather” (Sunny, Rainy), “Temperature” (Hot, Cold), and the target variable “Play” (Yes, No).

• Root Node: The algorithm starts with the entire dataset and looks at all possible splits. Suppose “Weather” is chosen as the best split.
  • Sunny: Now, within the subset where the weather is sunny, the algorithm looks for the next best feature to split on, say “Temperature”.
    • Hot: No (leaf node)
    • Cold: Yes (leaf node)
  • Rainy: The data points with rainy weather are evaluated next.
    • Wind: Further split by “Wind” (Weak, Strong).
      • Weak: Yes (leaf node)
      • Strong: No (leaf node)

The decision path for a new instance would start at the root node and follow the branches based on the attribute values until reaching a leaf node, which provides the prediction.

4. Stopping Criteria

To prevent overfitting, decision trees use various stopping criteria:

• Maximum Depth: Limits the depth of the tree.
• Minimum Samples for a Split: Ensures a node must have a minimum number of samples before splitting.
• Minimum Samples for a Leaf: Ensures each leaf node must have a minimum number of samples.
• Early Stopping: Stops tree growth when the splits do not result in significant information gain.

5. Pruning

Pruning enhances the model by removing parts of the tree that do not provide significant power. This can be done in two ways:

• Pre-Pruning (Early Stopping): Halt tree growth early by setting limits.
• Post-Pruning: Grow the tree fully and remove nodes that do not improve model performance.

Decision trees work by splitting the data into smaller subsets based on the most informative attributes, continuing this process until the subsets are sufficiently homogeneous or predefined criteria are met. This structured approach helps create intuitive and effective models for making predictions in various contexts.

Types of Decision Trees

Decision trees come in two main types: classification trees and regression trees. Each type is designed to handle different kinds of predictive modelling problems.

1. Classification Trees

Classification trees are used when the target variable is categorical. A classification tree aims to classify the data points into predefined classes or categories. Each internal node in a classification tree tests an attribute and branches based on the attribute’s values, and each leaf node represents a class label.

Example Use Case

Consider a dataset with patient information (age, symptoms, test results), where the target variable is whether the patient has a particular disease (Yes/No). A classification tree would help predict the presence of the disease based on the patient’s data.

How It Works

• Splitting Criteria: Common criteria for splitting nodes include Gini impurity, entropy (information gain), and chi-square.
• Decision Path: The tree is traversed from the root node to a leaf node, where each decision is based on the value of an attribute. This ultimately leads to a class label at the leaf node.

2. Regression Trees

Regression trees are used when the target variable is continuous. The goal of a regression tree is to predict the value of the target variable based on the input features. Each internal node represents a decision on an attribute, and each leaf node represents a continuous value, which is the average value of the target variable for the data points in that leaf.

Example Use Case

Consider a dataset with features like house size, location, and number of bedrooms, where the target variable is the house price. A regression tree would help predict the house price based on the input features.

How It Works

• Splitting Criteria: Common criteria for splitting nodes include variance reduction, mean squared error (MSE), and mean absolute error (MAE).
• Decision Path: The tree is traversed from the root node to a leaf node, with decisions based on attribute values ultimately leading to a predicted continuous value at the leaf node.

Differences Between Classification and Regression Trees

• Target Variable: Classification trees deal with categorical targets, while regression trees deal with continuous targets.
• Splitting Criteria: Classification trees use measures like Gini impurity and entropy, whereas regression trees use measures like variance reduction and MSE.
• Output: The output of classification trees is a class label, while the output of regression trees is a continuous value.

Examples of Problems Solved by Classification and Regression Trees

• Classification Trees:
  • Email spam detection (Spam/Not Spam)
  • Customer churn prediction (Churn/No Churn)
  • Medical diagnosis (Disease/No Disease)
• Regression Trees:
  • House price prediction
  • Stock price forecasting
  • Sales forecasting

Visualization of Decision Trees

Both classification and regression trees can be visualized as hierarchical diagrams. This visualization helps the user understand the model’s decision-making process, making interpreting and explaining the results easier.

Choosing the Right Type of Decision Tree

The choice between a classification tree and a regression tree depends on the nature of the target variable in your dataset:

• Use a classification tree if your target variable is categorical and you need to assign data points to specific classes.
• Use a regression tree if your target variable is continuous and you must predict a numeric value.

Understanding the types of decision trees and their specific use cases is crucial for selecting the appropriate model for your data analysis. Classification trees excel in categorical predictions, while regression trees are mighty for continuous value predictions, making decision trees a versatile tool in the machine learning toolbox.

How To Build a Decision Tree In Machine Learning With Python

Building a decision tree involves several steps, from data preprocessing to choosing splitting criteria and pruning techniques. Here’s a detailed guide on how to build a decision tree effectively:

1. Data Preprocessing

Before constructing a decision tree, it’s essential to preprocess the data to ensure it is clean and suitable for analysis. Key preprocessing steps include:

• Handling Missing Values: Replace or remove missing values to avoid errors during tree construction.
• Encoding Categorical Variables: Convert categorical variables into numerical values using one-hot encoding or label encoding techniques.
• Feature Scaling: While not always necessary for decision trees, scaling can be helpful when combining decision trees with other algorithms.

2. Splitting Criteria

Choosing the proper splitting criterion is crucial for building an effective decision tree. Different criteria are used depending on whether the task is classification or regression:

For Classification Trees:

• Gini Impurity: Measures the impurity of a node. A lower Gini impurity indicates a purer node.
• Entropy (Information Gain): Measures the disorder or impurity in the dataset. Information gain is the reduction in entropy.

For Regression Trees:

Variance Reduction: Measures how much the variance is reduced by splitting the node.

3. Pruning Techniques

Pruning is essential to avoid overfitting and improve the generalizability of the decision tree. There are two main types of pruning:

• Pre-Pruning (Early Stopping): Stops the tree growth early by setting constraints during the construction phase.
  • Maximum Depth: Limits the depth of the tree.
  • Minimum Samples for a Split: Ensures that a node must have a minimum number of samples before splitting.
  • Minimum Samples for a Leaf: Ensures that each leaf node has a minimum number of samples.
• Post-Pruning: Involves growing the tree fully and then removing nodes that do not contribute significantly.
  • Cost Complexity Pruning (CCP): Removes nodes based on a cost-complexity criterion, balancing the tree’s size and accuracy.

4. Handling Overfitting 

Overfitting occurs when the decision tree captures noise in the training data rather than the underlying pattern. To handle overfitting:

• Use Pruning: Both pre-pruning and post-pruning techniques help reduce overfitting.
• Set Constraints: Limit the maximum depth, minimum samples per split, and minimum samples per leaf.
• Cross-Validation: Use techniques like k-fold cross-validation to evaluate the model’s performance on different subsets of the data.

Build the Decision Tree Model In Python

Here’s a step-by-step guide to building a decision tree using a popular library like Scikit-learn in Python:

# Import necessary libraries
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score, mean_squared_error

# Load the dataset
# Assume X is the feature set and y is the target variable
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)

# Choose the model type based on the task
# For classification
model = DecisionTreeClassifier(criterion='gini', max_depth=5, random_state=42)
# For regression
# model = DecisionTreeRegressor(criterion='mse', max_depth=5, random

Top 5 Decision Tree Algorithms

Decision tree algorithms are at the heart of building decision trees. There are several algorithms used to construct decision trees, each with its unique characteristics and approaches. Here’s an overview of some popular decision tree algorithms:

1. ID3 (Iterative Dichotomiser 3)

• Type: Classification
• Main Idea: The ID3 algorithm uses entropy and information gain to construct decision trees. It iteratively selects the best attribute to split the dataset based on the highest information gain until all data points belong to the same class or no attributes are left.
• Limitations: It cannot handle continuous-valued attributes and may create overfitting trees due to its greedy nature.

2. C4.5 (Classification and Regression Tree)

• Type: Classification and Regression
• Main Idea: C4.5 is an extension of ID3 and addresses some of its limitations. It can handle both categorical and continuous attributes. Instead of information gain, it uses gain ratio as the splitting criterion, which penalizes attributes with many values.
• Handling Missing Values: C4.5 can handle missing values by assigning probabilities to each possible value based on the distribution of the other values in the dataset.

3. CART (Classification and Regression Trees)

• Type: Classification and Regression
• Main Idea: CART is a widely used algorithm for both classification and regression tasks. It uses Gini impurity for classification and mean squared error (MSE) for regression to determine the best splits. CART constructs binary trees (each internal node has two children) and recursively splits the dataset into subsets.
• Pruning: CART supports both pre-pruning and post-pruning techniques to prevent overfitting. It prunes the tree using cost-complexity pruning, which balances its complexity and ability to classify or predict accurately.
• Handling Missing Values: CART can handle missing values by creating surrogate splits. Surrogate splits act as backups for primary splits when data is missing for some observations.

4. Random Forest

• Type: Ensemble Learning (Combination of Decision Trees)
• Main Idea: Random Forest is not a single decision tree algorithm but a collection of decision trees trained on random subsets of the data (bagging) and random subsets of features (feature bagging). Each tree in the forest votes on the final prediction, and the mode (for classification) or average (for regression) of these predictions is taken as the final output.
• Advantages: Random Forest reduces overfitting and improves the model’s generalization ability by averaging the predictions of multiple trees. It also provides feature importance scores, indicating the contribution of each feature to the model’s performance.
• Applications: Random Forest is widely used in various fields, including finance, healthcare, and bioinformatics, due to its robustness and scalability.

5. Gradient Boosted Trees

• Type: Ensemble Learning (Boosting)
• Main Idea: Gradient Boosted Trees is another ensemble learning technique that combines multiple weak learners (shallow decision trees) to create a strong learner. It trains trees sequentially, with each subsequent tree correcting the errors of the previous ones. Adding new trees to the ensemble minimizes a loss function (e.g., mean squared error for regression, deviance for classification).
• Advantages: Gradient-boost Trees often outperform other algorithms in terms of predictive accuracy. Compared to random forests, they are less prone to overfitting but can be computationally expensive.
• Applications: Gradient-boosted trees are used in tasks such as web search ranking, anomaly detection, and recommendation systems.

Advantages and Disadvantages of Decision Trees

Decision trees offer several advantages and disadvantages that impact their suitability for different machine learning tasks. Understanding these pros and cons is essential for effectively utilizing decision trees in data analysis and model development.

Advantages of Decision Trees

1. Interpretability: Decision trees mimic human decision-making processes, making them easy to understand and interpret. The tree structure provides clear insights into the decision-making logic, making them particularly useful for explaining predictions to stakeholders and domain experts.
2. Versatility: Decision trees can handle both numerical and categorical data, making them suitable for a wide range of applications. They can be applied to both classification and regression tasks, offering flexibility in modelling various types of data and predicting different types of outcomes.
3. Non-linearity: Decision trees can capture non-linear relationships between features and the target variable. Unlike linear models, which assume linear relationships, decision trees can effectively model complex decision boundaries and interactions between features.
4. Feature Importance: Decision trees provide a measure of feature importance, indicating the relative importance of each feature in predicting the target variable. This information can be used for feature selection, identifying key drivers of the outcome, and gaining insights into the underlying data patterns.
5. Robustness to Outliers and Irrelevant Features: Decision trees are robust to outliers and noise in the data. They can effectively handle datasets with missing values and irrelevant features without requiring extensive preprocessing.

Disadvantages of Decision Trees

1. Overfitting: Decision trees are prone to overfitting, especially when they are allowed to grow too deep or when the dataset is small. Deep decision trees can memorize the training data, capturing noise and outliers, which reduces their generalization performance on unseen data.
2. High Variance: Decision trees are sensitive to slight variations in the training data, leading to high variance models. This sensitivity can result in different trees being generated for slightly different training datasets, making the model less stable and more challenging to interpret.
3. Bias Toward Dominant Features: Decision trees tend to favour features with more levels or categories, leading to biased splits that may not capture the authentic underlying relationships in the data. Features with many levels may dominate the decision-making process, overshadowing potentially important but less frequent features.
4. Limited Expressiveness: While decision trees can capture complex decision boundaries, they may struggle with capturing more intricate patterns present in the data. Other machine learning algorithms, such as neural networks, may offer better performance for tasks involving highly complex relationships.
5. Difficulty with Imbalanced Data: Decision trees may not perform well on imbalanced datasets, where one class significantly outnumbers the others. They tend to favour majority classes, leading to biased predictions and poor performance of minority classes.

7 Practical Tips For Enhancing Decision Trees

While decision trees are powerful models on their own, several techniques can enhance their performance, robustness, and generalization ability. In this section, we’ll explore some of these techniques and how they can be applied to improve decision tree models:

1. Ensemble Methods

Random Forest

Random Forest is an ensemble learning technique that builds multiple decision trees during training and combines their predictions through averaging or voting. This approach reduces overfitting and improves model robustness by leveraging the collective wisdom of various trees.

Gradient Boosted Trees

Gradient Boosted Trees sequentially train decision trees, with each tree correcting the errors of the previous ones. By combining weak learners with a strong learner, Gradient Boosted Trees achieve high predictive accuracy and are less prone to overfitting compared to individual decision trees.

2. Feature Engineering in Decision Trees

Feature Selection

Identifying and selecting relevant features can improve decision tree performance and interpretability. Techniques such as univariate feature selection, recursive feature elimination, or feature importance scores can help identify the most informative features.

Feature Transformation

Transforming features using techniques such as scaling, normalization, or polynomial expansion can improve decision tree performance, especially when dealing with features of different scales or non-linear relationships.

3. Pruning Techniques for Decision Trees

Pre-Pruning

Pre-pruning involves setting constraints on tree growth during the construction phase to prevent overfitting. Common pre-pruning techniques include limiting the maximum tree depth, minimum samples per split, or maximum leaf nodes.

Post-Pruning

Post-pruning involves growing the tree fully and then pruning nodes that do not improve model performance. Techniques such as cost-complexity pruning (CCP) or reduced error pruning can help improve the generalization ability of decision tree models.

4. Handling Imbalanced Data for Decision Trees

Balanced Sampling

When dealing with imbalanced datasets, balancing techniques such as oversampling minority classes, undersampling majority classes, or generating synthetic samples can help improve decision tree performance.

Class Weighting

Assigning higher weights to minority classes during model training can help mitigate the effects of class imbalance and improve the model’s ability to predict minority class instances.

5. Hyperparameter Tuning for Decision Trees

Grid Search

Exhaustive search over a specified hyperparameter grid to find the optimal combination of hyperparameters that maximize model performance.

Random Search

This method randomly samples hyperparameters from specified distributions and evaluates their performance, allowing for more efficient exploration of the hyperparameter space.

Bayesian Optimization

Sequential model-based optimization technique that uses probabilistic models to select the next set of hyperparameters to evaluate based on the results of previous evaluations.

6. Handling Missing Values

Imputation

Replace missing values with a suitable estimate, such as the mean, median, or mode of the feature, or use more sophisticated imputation techniques, such as k-nearest neighbours (KNN) or predictive modelling.

7. Feature Importance Analysis

Gini Importance

Measure the importance of each feature based on the decrease in Gini impurity when splitting on that feature. Features with higher Gini importance are more informative for predicting the target variable.

Permutation Importance

Assess feature importance by measuring the decrease in model performance (e.g., accuracy or mean squared error) when the values of a feature are randomly shuffled. Features that result in more significant performance drops are considered more important.

Enhancing decision tree models through ensemble methods, feature engineering, pruning, hyperparameter tuning, and handling imbalanced data can significantly improve their performance, robustness, and generalization ability. By leveraging these techniques effectively, practitioners can build decision tree models that achieve higher predictive accuracy and provide valuable insights for decision-making across various domains.

Alternative Algorithms To Consider Than Decision Trees

Decision trees are powerful machine learning models, but they are not the only option available. In this section, we’ll explore how decision trees compare to other machine learning algorithms and in which contexts decision trees excel or may be outperformed.

1. Logistic Regression

• Decision Trees: Decision trees are non-parametric models that partition the feature space into regions and make predictions based on majority voting or averaging at leaf nodes.
• Logistic Regression: Logistic regression is a linear model that estimates the probability of a binary outcome based on a linear combination of input features, followed by a sigmoid transformation.
• Comparison: Decision trees are more flexible and can capture non-linear relationships between features and the target variable, while logistic regression assumes a linear relationship between features and the log-odds of the outcome. Decision trees may perform better when dealing with complex decision boundaries or interactions between features. At the same time, logistic regression may be more interpretable and suitable for binary classification tasks with linear relationships.

2. Support Vector Machines (SVMs)

• Decision Trees: Decision trees partition the feature space into regions based on feature values and make predictions based on majority voting or averaging at leaf nodes.
• Support Vector Machines (SVMs): SVMs find the hyperplane that maximally separates the data points of different classes in the feature space, using a kernel function to map the data into a higher-dimensional space if necessary.
• Comparison: Decision trees are more interpretable and can handle both classification and regression tasks, while SVMs are adequate for binary classification tasks with complex decision boundaries. Decision trees may be more suitable for tasks where interpretability is essential or when dealing with datasets with non-linear relationships. At the same time, SVMs may be preferred for tasks with high-dimensional data or when maximizing margin is crucial.

3. Neural Networks

• Decision Trees: Decision trees partition the feature space into regions based on feature values and make predictions based on majority voting or averaging at leaf nodes.
• Neural Networks: Neural networks consist of interconnected layers of nodes (neurons) that learn hierarchical representations of the data through forward and backward propagation of signals.
• Comparison: Neural networks are highly flexible and can capture complex non-linear relationships between features and the target variable, but they are often black-box models and may be challenging to interpret. Decision trees, on the other hand, are more interpretable and can provide insights into the decision-making process, but they may struggle with capturing complex patterns present in the data. The choice between decision trees and neural networks depends on factors such as interpretability requirements, dataset size, and complexity of relationships.

4. Ensemble Methods (Random Forests, Gradient Boosted Trees)

• Decision Trees: Decision trees are individual models that make predictions based on partitioning the feature space into regions and making decisions at each node.
• Ensemble Methods: Ensemble methods combine multiple individual models (e.g., decision trees) to improve predictive performance through averaging or voting.
• Comparison: Ensemble methods such as Random Forests and gradient-boosted trees often outperform individual decision trees by reducing overfitting and improving generalization ability. They combine the strengths of multiple decision trees while mitigating their weaknesses, resulting in more robust and accurate models. Decision trees may be preferred when interpretability is paramount or when dealing with small to medium-sized datasets. In contrast, ensemble methods may be selected for tasks where maximizing predictive accuracy is crucial.

Decision trees are versatile machine learning models that can be compared and contrasted with other algorithms such as logistic regression, support vector machines, neural networks, and ensemble methods. The choice of algorithm depends on factors such as the nature of the data, interpretability requirements, predictive accuracy goals, and computational resources available. By understanding the strengths and weaknesses of each algorithm, we can select the most appropriate model for their specific task and maximize the effectiveness of their machine learning solutions.

Conclusion

Decision trees are versatile and valuable tools in machine learning, offering a balance between interpretability and predictive power. They excel in scenarios where transparency in decision-making is crucial, such as medical diagnosis or credit risk assessment. However, they also have limitations, such as susceptibility to overfitting and difficulty capturing complex relationships in the data.

Despite their limitations, decision trees can be enhanced through pruning, ensemble methods, and feature engineering to improve their performance and generalization ability. Moreover, decision trees play a vital role in the broader landscape of machine learning, complementing other algorithms such as logistic regression, support vector machines, and neural networks.

When deciding whether to use decision trees or other algorithms, consider factors such as the model’s interpretability, the data’s complexity, and the specific requirements of the task at hand. While decision trees may be preferred for their transparency and ease of understanding, ensemble methods like Random Forests or gradient-boosted trees could be optimal for maximizing predictive accuracy and robustness.

Ultimately, selecting the appropriate algorithm depends on the trade-offs between interpretability, accuracy, and computational efficiency, as well as the project’s specific objectives. By understanding the strengths and weaknesses of decision trees and other algorithms, practitioners can make informed decisions to develop effective machine learning solutions tailored to their needs.