8 Pros of Decision Tree Regression in Machine Learning
Updated on Feb 25, 2025 | 16 min read | 15.4k views
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Updated on Feb 25, 2025 | 16 min read | 15.4k views
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Decision tree regression in machine learning is a model that predicts continuous values by learning decision rules from data features. While it is simple and interpretable, it has its own set of challenges.
In this blog, we’ll explore the pros of decision tree regression in machine learning, along with some key disadvantages of decision tree in machine learning to provide a balanced perspective.
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Decision tree regression in machine learning is a powerful algorithm for predicting continuous values. It’s particularly useful in various scenarios, such as forecasting, predicting house prices, or estimating sales based on historical data.
Let’s examine the pros of decision tree regression in machine learning, starting with one of its key advantages: interpretability.
One of the biggest advantages of decision tree regression is its transparency. Unlike black-box models such as neural networks, decision trees are easy to interpret and understand. You can easily visualize a decision tree’s structure, which is often a series of decisions based on input features.
This makes it ideal for situations where you need to explain how the model arrived at a prediction, such as in business, healthcare, or finance.
Decision tree regression handles continuous values by recursively splitting data based on features that minimize Mean Squared Error (MSE), selecting optimal splits to reduce variance and improve prediction accuracy.
Example:
Let’s say you have a dataset of house prices. A decision tree might first ask if the house is in a city or suburban area. If the answer is "city," it could then ask how many bedrooms the house has, and based on that, predict the price.
This simplicity makes decision tree regression an attractive choice when interpretability is essential for decision-making or regulatory compliance.
Explore upGrad’s Machine Learning Courses to master decision tree regression and its practical applications in real-world scenarios.
Next, let’s see how decision trees easily handle different data types, adding to their versatility.
One of the standout features of decision tree regression is its ability to handle both categorical and numerical data with minimal preprocessing. This versatility makes it an excellent choice for various real-world applications where data is often messy and diverse.
Unlike other models that require extensive feature engineering or data transformation, decision trees can naturally work with different data types.
Handling Numerical Data
Decision trees easily handle numerical data by finding the best split at each decision node based on a threshold value. For example, if you're predicting house prices, a decision tree might split the data based on the value of square footage or price range.
Example:
from sklearn.tree import DecisionTreeRegressor
# Sample data
X = [[1500], [2500], [3000], [3500], [4000]] # Square footage
y = [400000, 600000, 650000, 700000, 750000] # House prices
# Fit decision tree
model = DecisionTreeRegressor()
model.fit(X, y)
In this example, the decision tree splits the data based on square footage (numerical value), determining the predicted price range.
Handling Categorical Data
For categorical data, decision trees treat each category as a unique value. Whether it’s the type of house (single-family, townhouse, etc.) or the neighborhood, Pig Latin or any other categorical attribute, decision trees can split the data based on categories and handle them efficiently.
Example:
from sklearn.tree import DecisionTreeClassifier
# Sample data
X = [['red'], ['blue'], ['green'], ['red'], ['blue']] # Color of item
y = [0, 1, 1, 0, 1] # Purchase decision
# Fit decision tree
model = DecisionTreeClassifier()
model.fit(X, y)
In this example, the decision tree splits the data based on color (categorical attribute), assigning a specific label (purchase decision) based on the color.
Benefits of Handling Mixed Data Types:
Let's see how decision trees skip the need for feature scaling, simplifying the process.
One of the key advantages of decision tree regression in machine learning is that it does not require feature scaling or normalization. This distinguishes decision trees from other algorithms like support vector machines (SVM), k-nearest neighbors (KNN), and logistic regression, which rely heavily on normalized data for optimal performance.
With decision trees, the algorithm works directly with the raw data, regardless of the features' magnitude or range, simplifying the entire preprocessing pipeline.
Decision trees split data at various thresholds, selecting the best feature at each node to make decisions. Since they make decisions based on splitting the data (for example, splitting based on a threshold value like "age > 30"), they are not affected by the scale of the features.
The importance lies in the ability to separate data into distinct subsets, not the relative size of the values.
Example:
Let’s say we have a dataset with two features: age (ranging from 10 to 100) and income (ranging from INR 10,000 to INR 100,000). Decision trees can still make effective splits without scaling based on these values.
Here’s an example:
from sklearn.tree import DecisionTreeRegressor
# Sample data
X = [[25, 20000], [30, 50000], [45, 100000], [60, 70000]] # [Age, Income]
y = [100000, 150000, 200000, 175000] # Target (House price)
# Fit decision tree regressor
model = DecisionTreeRegressor()
model.fit(X, y)
In this example:
Benefits:
Also Read: Difference Between Linear and Logistic Regression: A Comprehensive Guide for Beginners in 2025
Next, let’s dive into how decision trees easily handle non-linear relationships.
In decision tree regression, the data is split at various points based on feature values, creating a tree structure where each split reflects a decision made on the data. These splits allow decision trees to model non-linear interactions because they do not assume a specific functional relationship between features and the target variable.
Instead, they create multiple decision boundaries that reflect the actual data distribution.
Example:
Let’s say you are predicting the price of a house based on two features: square footage and the age of the house. These two features may not have a linear relationship with the price—larger houses might not always be more expensive, and older houses could have varying values based on other factors.
A decision tree can model this non-linear relationship by splitting the data based on thresholds such as:
Here’s a simple Python example:
from sklearn.tree import DecisionTreeRegressor
# Sample data: [Square footage, Age of house]
X = [[1500, 30], [2000, 20], [2500, 15], [3000, 50], [3500, 5]]
y = [400000, 450000, 600000, 650000, 700000]
# Fit decision tree regressor
model = DecisionTreeRegressor()
model.fit(X, y)
In this case, the decision tree splits the data into subgroups based on the square footage and age of the houses. These splits allow the model to capture the complex, non-linear relationships between these features and the target house price.
Key Benefits:
Also Read: How to Create Perfect Decision Tree | Decision Tree Algorithm [With Examples]
Next, let’s explore how decision trees handle outliers, making them resilient to noise in the data.
When training a decision tree, each decision point (split) is chosen based on a feature's threshold value that best separates the data into distinct groups. Since the decision tree’s goal is to partition the data effectively, outliers typically fall into smaller branches where their impact is minimized. This prevents the outliers from influencing the entire model.
Example: Let’s say you are predicting house prices based on features like square footage and number of bedrooms. If one data point has a massive house that is a clear outlier (e.g., a mansion with 100+ rooms), the decision tree will place that mansion into a separate branch without letting it skew the overall model.
Here’s a simple example where outliers are included in the dataset:
from sklearn.tree import DecisionTreeRegressor
# Sample data with an outlier (extremely high value)
X = [[1500, 3], [2000, 3], [2500, 4], [3000, 5], [10000, 10]] # Last row is the outlier
y = [300000, 400000, 500000, 600000, 1000000] # Target values
# Fit decision tree regressor
model = DecisionTreeRegressor()
model.fit(X, y)
In this case:
The outlier at [10000, 10] doesn’t influence the main structure of the tree as much. Instead, the tree focuses on creating splits based on more relevant data points, ignoring the extreme value to a certain extent.
Benefits of Robustness to Outliers:
Also Read: Outlier Analysis in Data Mining: Techniques, Detection Methods, and Best Practices
Now, let’s dive into how decision trees efficiently handle missing values.
In decision trees, if a value is missing for a particular feature, the model doesn’t discard the data point. Instead, it uses the next best feature (called a surrogate split) to continue making the decision. This allows the model to work with incomplete data efficiently.
Example:
Suppose you're predicting the salary based on the features years of experience and education level. If education level is missing, the decision tree uses years of experience as a proxy, ensuring predictions remain unaffected.
from sklearn.tree import DecisionTreeRegressor
import numpy as np
# Sample data with missing values
X = [[2, np.nan], [5, 3], [8, 4], [10, np.nan], [12, 6]] # Second column has missing values
y = [30000, 50000, 60000, 70000, 80000]
# Fit decision tree regressor
model = DecisionTreeRegressor()
model.fit(X, y)
In this case:
Benefits:
Also Read: The Data Science Process: Key Steps to Build Data-Driven Solutions
Let’s move on to another strength of decision trees – their non-parametric nature.
Decision tree regression is a non-parametric model, meaning it doesn’t make any assumptions about the underlying data distribution. Unlike parametric models, which assume a certain form (like a linear relationship), decision trees can adapt to the shape of the data without needing to fit a predefined model.
This flexibility allows them to model complex patterns and relationships in the data without worrying about the underlying statistical assumptions.
Why Non-Parametric Matters:
Example: Suppose you're working with a dataset where you want to predict customer spending behavior based on features like age and income.
A decision tree captures non-linear complexity in spending without assuming a specific data distribution.
from sklearn.tree import DecisionTreeRegressor
# Sample data with non-linear relationships
X = [[25, 20000], [40, 50000], [60, 100000], [75, 150000]]
y = [20000, 35000, 70000, 90000] # Target variable: Spending behavior
# Fit decision tree regressor
model = DecisionTreeRegressor()
model.fit(X, y)
Here, decision trees don’t assume a linear relationship between age, income, and spending behavior. Instead, the model adapts to the data and creates splits that best capture the data's non-linear structure.
Benefits:
Also Read: Types of Probability Distribution [Explained with Examples]
Now let’s look at how they combine multiple features to make predictions more accurate.
Decision trees can handle multiple features by using them at various decision points to split data, enabling them to make predictions based on the best combination of features. Instead of relying on one feature, decision trees can combine multiple features to improve prediction accuracy. This is especially beneficial when data has many interacting factors.
How Decision Trees Combine Features:
At each node, a decision tree evaluates all available features to determine the best split that minimizes the error. By using multiple features in this way, the tree captures complex interactions between them and builds more accurate predictions.
Example: If you’re predicting house prices, a decision tree might use both square footage and location to split the data, with each feature playing a role in determining the final prediction.
from sklearn.tree import DecisionTreeRegressor
# Sample data with multiple features
X = [[1500, 'suburban'], [2500, 'urban'], [3000, 'urban'], [2000, 'suburban']]
y = [400000, 500000, 600000, 450000] # Target: House prices
# Fit decision tree regressor
model = DecisionTreeRegressor()
model.fit(X, y)
In this case:
Benefits:
While decision trees offer many advantages, let's now look at some of their disadvantages and when you might want to consider alternatives.
While decision trees offer several pros of decision tree regression in machine learning, they also have some notable disadvantages of decision trees in machine learning. Understanding these drawbacks is crucial for making informed decisions about when to use decision trees and when to consider other models.
One of the most common issues with decision trees is their tendency to overfit the data. Overfitting occurs when the tree becomes too complex and captures noise or random fluctuations in the training data rather than general patterns. This leads to poor performance on new, unseen data.
Also Read: What is Overfitting & Underfitting In Machine Learning ? [Everything You Need to Learn]
Another drawback of decision trees is their instability. Small changes in the data can lead to significant changes in the structure of the tree. This is because each split is based on a small portion of the data, and even minor variations can cause the tree to produce different splits, leading to inconsistent predictions.
Also Read: Understanding Machine Learning Boosting: Complete Working Explained for 2025
Decision trees tend to favor the majority classes in imbalanced datasets. This means that if the data contains a dominant class (for example, 90% of your data might belong to one class), the tree may over-predict that class while under-predicting the minority class, leading to biased predictions.
Training large decision trees can be computationally expensive and time-consuming, especially when working with large datasets. As the tree grows deeper, the number of possible splits increases, and this can require more computational resources and time.
Also Read: Everything You Should Know About Unsupervised Learning Algorithms
The more you explore decision tree regression in machine learning, the more proficient you'll become in using decision trees to model complex data relationships, enabling you to build accurate, interpretable models across diverse machine learning tasks.
With industry-relevant curriculum and hands-on projects, upGrad ensures you're equipped with the knowledge and skills needed to implement decision trees effectively.
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