A practical introduction to machine learning for people who work with data
This guide assumes you are comfortable with basic KNIME operations like reading files, filtering data, and joining tables, but have never touched machine learning before. We will build up from the simplest concepts to neural networks, explaining not just how but why you would use each technique.
Machine learning can sound intimidating, but it is really about getting computers to find patterns in data and make predictions. If you have ever used Excel's trendline feature or made forecasts based on historical data, you have already done the thinking that machine learning automates.
You will find [screenshot placeholder] and [workflow file] markers throughout. Add your own as you work through examples.
Machine learning is teaching computers to make decisions or predictions based on patterns in data, without explicitly programming every rule. Think of it like this: instead of writing "if temperature above 30°C then ice cream sales equal high", you show the computer thousands of examples of temperature and ice cream sales, and it figures out the relationship itself.
This matters because real-world patterns are usually too complex for simple rules. Customer churn is not just about one factor. Equipment failure depends on dozens of variables interacting in ways that would take forever to code manually. Fraudulent transactions have subtle patterns that humans miss but algorithms can spot. Machine learning finds these complex patterns automatically.
Here is the key insight that changes everything: you do not need a perfect model. You just need to beat your business baseline, whether that is your current rules, manual processes, or simple heuristics. For fraud detection, if your current rules catch 60% of fraud while blocking 10% of legitimate transactions, a model that catches 75% while blocking only 8% is a significant win. Even modest improvements over baseline performance translate to real business value.
The beauty of machine learning in KNIME is its consistency. Every algorithm follows the same basic pattern: prepare your data, split it for testing, train a model with a Learner node, make predictions with a Predictor node, and measure success with a Scorer node. Once you understand this workflow structure, you can apply it to any algorithm from simple decision trees to complex neural networks.
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Basic ML workflow structure: Data → Partitioning → Learner → Predictor → Scorer
[workflow file]
Link: ML_Basic_Template.knwf
Before building any model, you must establish your baseline. This is what you would achieve with current methods or the simplest possible approach. Without this benchmark, you cannot know if your sophisticated model actually improves anything.
The baseline depends entirely on your problem type. For balanced binary classification where classes are roughly 50/50, uniform random guessing gets 50% accuracy. This sets a low bar that any reasonable model should clear easily. For imbalanced classification where one class dominates, the baseline is much trickier. If 95% of transactions are legitimate and 5% are fraud, always guessing "legitimate" achieves 95% accuracy while being completely useless for fraud detection.