Estimate node disorder from class counts quickly. Check entropy, information gain, and gain ratio clearly. Turn raw outcomes into practical model insights for decisions.
Enter class labels, parent node counts, and optional branch counts. The calculator can estimate entropy, split quality, and majority-based accuracy lift.
This sample shows a parent node and three candidate branches for four outcome classes.
| Node / Branch | Class A | Class B | Class C | Class D | Total |
|---|---|---|---|---|---|
| Parent Node | 40 | 30 | 20 | 10 | 100 |
| Branch 1 | 20 | 5 | 5 | 0 | 30 |
| Branch 2 | 15 | 10 | 5 | 5 | 35 |
| Branch 3 | 5 | 15 | 10 | 5 | 35 |
Entropy(S) = -Σ p(i) × log2(p(i))
Entropy measures uncertainty inside a node. A pure node has entropy near zero. A mixed node has higher entropy.
Gain = Entropy(Parent) - Σ (|Child| / |Parent|) × Entropy(Child)
Information gain shows how much uncertainty a split removes. Larger values indicate a more useful split.
SplitInfo = -Σ (|Child| / |Parent|) × log2(|Child| / |Parent|)
Split information measures how broadly the data is divided across branches.
GainRatio = InformationGain / SplitInfo
Gain ratio penalizes splits that create many uneven branches without adding much predictive value.
Gini = 1 - Σ p(i)^2
Classification Error = 1 - max(p(i))
These supporting metrics help compare split quality from different angles.
EVPI means expected value of perfect information. First, calculate the best expected value without extra information. Next, calculate the expected value when each uncertain state is known in advance. Then subtract the original best expected value from the perfect-information value. The difference is EVPI.
Accuracy is the number of correct predictions divided by total predictions. Use (TP + TN) / Total for binary classification, or sum all correct predictions on the confusion matrix diagonal and divide by all samples for multiclass problems.
Entropy shows how mixed a node is. A lower value means the node is purer, while a higher value means the classes are more evenly mixed and harder to separate.
Information gain measures how much uncertainty drops after a split. It helps identify which feature or branch separates the target classes most effectively.
Use gain ratio when you want to reduce bias toward splits with many branches. It normalizes information gain by the split’s own complexity.
There is no universal perfect value. Lower entropy is usually better because it means the node is more class-pure. Compare values across candidate splits instead of using a fixed threshold.
Yes. This file supports four classes directly. You can rename them to fit productivity, operations, quality, or forecasting use cases.
Parent impurity metrics still work, but split metrics like information gain and gain ratio are only reliable when all branch totals add up to the same parent total.
Entropy is useful, but gini and classification error provide supporting views of impurity. Seeing all three can make split comparisons more practical and easier to explain.
It estimates accuracy if each node predicts its majority class only. This is a quick way to judge whether a split improves simple classification decisions.
Important Note: All the Calculators listed in this site are for educational purpose only and we do not guarentee the accuracy of results. Please do consult with other sources as well.