May 19, 2023

Decision Trees and Random Forests in KNIME

By John Emery

Today’s digital world is inundated with massive amounts of data. But in its raw form, this data is just noise until it is analyzed and transformed into meaningful information. This is where data science steps in. 

As an interdisciplinary field, data science leverages scientific methods, algorithms, and systems to extract insights from structured and unstructured data. The insights generated through data science are helping businesses to predict future trends, understand customer behavior, improve products, and make data-driven decisions.

One such powerful tool aiding in this transformative process is the KNIME Analytics Platform. KNIME offers an open-source, end-to-end data science environment that enables users to create data flows, manipulate data, prototype models, and so much more–without complex coding. 

Its visually appealing interface and the ability to add custom scripts in various programming languages make it a preferred choice among novice and seasoned data scientists. 

This post will delve into one of the many facets of KNIME’s capabilities–building predictive models using decision trees and random forests. These algorithms are not just fundamental to any data scientist’s toolkit, but they also form the backbone of many complex machine learning workflows. 

Understanding and mastering these techniques can unlock a deeper level of data analysis and predictive power.

building predictive

The Need for Predictive Modeling

Predictive modeling is a crucial element in the world of data analytics and machine learning. It holds the key to forecasting future outcomes based on historical data and current trends. 

Businesses across sectors–from finance to manufacturing, healthcare to the life sciences–use predictive modeling to optimize operations, manage risks, and make informed decisions, thereby gaining a competitive edge. 

This process leverages a variety of algorithms, two of which are decision trees and random forests.

Understanding Decision Trees and Random Forests

Decision Trees and Random Forests are two of the most commonly used algorithms in machine learning and data science, owing to their versatility, simplicity, and robustness. Let’s delve deeper into these two concepts:

Decision Trees

A decision tree is a supervised learning algorithm that can be used for both classification and regression analyses. It is called a ‘decision tree’ because it uses a tree-like model, where each decision branches from other decisions. It starts with a single node, which then splits into possible outcomes. 

Each of these outcomes leads to additional nodes, which branch off into other possibilities. This continues until a decision outcome is reached.

decision outcome

The beauty of decision trees lies in their simplicity and interpretability. The top node, also known as the root, represents the feature that provides the most significant information gain. As we move down the tree, we get a set of rules that lead to a certain decision. 

This sequence of rules is easy to follow and understand, making decision trees a favorite tool in decision analysis.

However, decision trees have a tendency to overfit, meaning they can become too tailored to the training data and perform poorly when presented with new, unseen data. This is where Random Forests come in.

Random Forests

Random Forests is a type of ensemble learning method where multiple learning models are used to solve a problem. In the case of random forests, the model creates a ‘forest’ of decision trees, typically trained on different subsets of the original data. 

The idea behind this is to leverage the power of ‘majority voting’ for classification tasks or to average for regression tasks.

A random forest algorithm randomly selects observations and features to build multiple decision trees. Then it aggregates the votes from different decision trees to decide the final class of the test object (for classification) or takes the average of outputs by different trees (for regression). 

This method of ‘ensembling’ helps to handle the overfitting problem faced by decision trees, making random forests a more accurate and robust method for prediction tasks.

Random Forests are more complex than single-decision trees. They can handle a large number of features and provide a reliable feature importance estimate. 

Despite being somewhat harder to interpret compared to individual decision trees due to their ensemble nature, they are widely appreciated in the data science community for their effectiveness and versatility.

Building Predictive Models in KNIME

If you have ever used KNIME, you know that building workflows–whether for simple data cleaning or complex machine learning analyses–is quite simple. No matter the task, it all begins with dragging nodes onto a canvas and connecting them.

When building a predictive model–decision trees, random forests, or any other model available in KNIME–the general steps remain the same.

  1. Connect to your data. Depending on the source of your data, you may use a variety of nodes here. The CSV Writer or Excel Writer for those flat file-based sources, or perhaps the Snowflake Connector if that is where your data lives.
  2. Perform any data preprocessing steps. This can vary significantly from workflow to workflow but generally includes filtering, sorting, writing calculations, and pivoting.
  3. Create training and testing sets using the Partitioning node. This node allows you to split your input data into two tables based on a set number or percentage of records.
  4. Connect to one of the many “learner” nodes using the training table created above. In KNIME, predictive modeling nodes come in two flavors: learners, which build a model, and predictors, which make predictions using the learned model on a new data set. We can use the Decision Tree Learner and Random Forest Learner for decision trees and random forests.
  5. Once you have built a predictive model, you can test it using the testing data (or some other set of new records)) using the corresponding predictor node (Decision Tree Predictor or Random Forest Predictor).
  6. Finally, you can score the model. Depending on the specific type of model, you may use one of several available scoring nodes. These nodes provide valuable information to evaluate the effectiveness of a predictive model.


KNIME’s strength lies in its intuitive interface, flexible functionalities, and reproducible workflows, making the task of building decision trees and random forest models fast and easy. While you may not understand all the mathematics involved in these models, understanding when to use a given model is most important.

KNIME offers a simplified approach to predictive modeling, where the complexity of coding is eliminated. Moreover, its capacity to handle large datasets and perform complex analyses within a short time gives it a distinct advantage. A novice KNIME developer can build the workflow above in less than five minutes.

With KNIME, you have a powerful tool at your disposal. Whether you’re a seasoned data scientist or a newcomer stepping into the world of data analytics, KNIME’s capabilities are bound to enhance your productivity and analytical prowess. 

So, why wait? Dive in, and start exploring the limitless possibilities that predictive modeling in KNIME provides.

If you want more information about decision trees and random forests in KNIME, contact our team of experts!

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