Why Learn This?
After learning the basics of decision trees, I wanted to understand how a machine learning workflow is actually built. In this exercise, I followed a KNIME demonstration that shows how data is prepared, split into training and testing sets, used to build a decision tree model, and then evaluated.
The goal is not just to create a model, but to understand the complete process from raw data to prediction.
KNIME Workflow Overview
Workflow:
CSV Reader → Number to String → Table Partitioner → Decision Tree Learner → Decision Tree View → Decision Tree Predictor → Scorer
A Histogram node is also connected to help visualize the data distribution.
What Each Node Does
| KNIME Node | Purpose |
|---|---|
| CSV Reader | Loads the dataset into KNIME. |
| Number to String | Converts a numeric column into a categorical (text) variable when needed for classification. |
| Table Partitioner | Splits the dataset into training data and testing data. The model learns from the training set and is evaluated using the test set. |
| Histogram | Displays the distribution of values in a selected variable. This helps with understanding the data before modeling. |
| Decision Tree Learner | Builds a decision tree model using the training data. The algorithm finds the best sequence of rules to classify the records. |
| Decision Tree View | Provides a visual representation of the generated decision tree, allowing us to see the branches and decision rules. |
| Decision Tree Predictor | Applies the trained model to the testing data and generates predictions. |
| Scorer | Compares the predicted values with the actual values and calculates the model’s performance. |
Simple Workflow Explanation
- Load the dataset.
- Prepare and transform the data.
- Split the data into training and testing sets.
- Train the decision tree model.
- Visualize the decision tree.
- Predict outcomes for the test data.
- Evaluate the prediction accuracy.
What I Learned
This exercise helped me understand that machine learning is much more than selecting an algorithm. A large part of the work involves preparing data, organizing the workflow, and evaluating the model after predictions are made.
I also learned that visual tools like KNIME make it easier to understand the logic behind machine learning models before writing code in Python.
Healthcare Analytics Perspective
Decision trees are widely used in healthcare analytics because they are easy to understand and explain. They can be applied to many types of classification problems, such as:
- Predicting whether a patient is at high or low risk.
- Identifying patients who may need additional care management.
- Supporting clinical decision-making based on patient characteristics.
- Classifying healthcare records into meaningful categories.
For healthcare professionals transitioning into data analytics, decision trees provide an intuitive introduction to machine learning because the model can be visualized as a series of simple decision rules.
Study Note: Machine learning workflows generally follow the same pattern: load data → prepare data → split data → train the model → make predictions → evaluate the results.