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On one synthetic admissions dataset, five classifiers scored between 90.50% and 93.00% accuracy on the same 200-row test set. That narrow range does not establish a generally best model. The more revealing result is the gap between training and test scores: the Decision Tree reached 100.00% training accuracy but 90.50% test accuracy, while the Random Forest also reached 100.00% on training and scored 93.00% on test data.
What the comparison tested
Ayush S Pangaonkar describes a binary classification task: predict whether a student is admitted using an entrance exam score and a percentage. The experiment used 1,000 synthetic students, with two features drawn uniformly from 0 to 100. These are generated examples, not records from an admissions institution.
The labels were generated from a logistic probability, 1 / (1 + exp(-(0.1*X1 + 0.2*X2 - 10))), and each outcome was sampled from a Bernoulli distribution using that probability. The resulting dataset contained 755 admitted and 245 not admitted students. The author used an 80/20 split: 800 training rows and 200 test rows, with random_state=42. All five models used the same split, which makes their reported scores comparable within this experiment.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteThe models were Logistic Regression, an RBF-kernel SVM, a Decision Tree, a Random Forest with 100 trees and entropy, and KNN. The figures below are those reported by Pangaonkar for this generated dataset and split; they are not independently reproduced results or general performance benchmarks. Read the author’s DEV Community article.
#1 Best Overall
How the reported scores compare
| Classifier | Training accuracy | Test accuracy | F1 for admitted students |
|---|---|---|---|
| Logistic Regression | 94.13% | 92.50% | 95.05% |
| SVM (RBF) | 93.75% | 91.50% | 94.46% |
| Decision Tree | 100.00% | 90.50% | 93.77% |
| Random Forest (100 trees, entropy) | 100.00% | 93.00% | 95.45% |
| KNN | 95.50% | 92.00% | 94.81% |
Test accuracy spans 2.5 percentage points, from 90.50% to 93.00%. With 200 test examples, one additional correct or incorrect prediction changes accuracy by 0.5 percentage points. Differences this small on a single split should not be treated as decisive evidence that one classifier is better.
What the training-to-test gap reveals
The Decision Tree’s perfect training accuracy did not carry over to the test set: its reported scores differ by 9.5 percentage points. That pattern is consistent with overfitting—the model fits the examples it learned from more closely than it predicts unseen examples. Pangaonkar describes the tree as having “memorized the training set.”
Rank #2
- Use scikit-learn to track an example ML project end to end
- Explore several models, including support vector machines, decision trees, random forests, and ensemble methods
- Exploit unsupervised learning techniques such as dimensionality reduction, clustering, and anomaly detection
- Dive into neural net architectures, including convolutional nets, recurrent nets, generative adversarial networks, autoencoders, diffusion models, and transformers
- Use TensorFlow and Keras to build and train neural nets for computer vision, natural language processing, generative models, and deep reinforcement learning
The Random Forest also scored 100.00% on training data, but its test accuracy was 93.00%, the highest of the five reported values. A perfect training score alone therefore does not tell you how well a model generalizes. The test result matters, and the gap is useful context for interpreting it.
Why the data generator matters
In this experiment, the probability used to create each label comes from a logistic function of the two input features. Logistic Regression is designed to model a relationship of this kind, so its strong result is plausible given how the synthetic labels were constructed. That is a property of this setup, not evidence that real admissions outcomes follow the same formula or that Logistic Regression is best for admissions data in general.
Rank #3
A classifier comparison is only as informative as the data and target it represents. If the real task has nonlinear relationships, noisy measurements, selection effects, or other patterns absent from a synthetic generator, these results may not transfer. The experiment is useful for illustrating a controlled comparison, not for choosing a model for a real admissions decision.
Why the experiment does not name a clear winner
Random Forest had the highest test accuracy and F1 for the admitted class in the reported table, while Logistic Regression was close behind. The differences are small relative to a test set of 200 examples, and the article reports only one train/test split. It does not give repeated-run results, uncertainty intervals, or a full confusion matrix. The author accordingly says, “I would not call Random Forest a clear winner over Logistic Regression from this experiment alone.”
Rank #4
To make a more robust comparison, repeat evaluation across multiple splits or use cross-validation, while keeping preprocessing and tuning confined to training data. Report the variability as well as an average score. This helps distinguish a consistent advantage from a small difference that depends on which examples happened to land in the test set.
The Tool Desk
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The dataset is imbalanced: 755 examples are admitted and 245 are not. Accuracy counts all correct predictions, but it can obscure how errors are distributed between classes. The article reports F1 for admitted students, but not class-specific precision and recall or a confusion matrix. Those missing measures prevent a fuller account of which kinds of errors each model made.
Best Value
- Precision for admitted: Of the students predicted admitted, how many were admitted?
- Recall for admitted: Of the admitted students, how many did the model identify?
- F1: A combined measure of precision and recall for a specified class; check which class it refers to.
- Confusion matrix: Counts true and false predictions by class, making the error trade-offs visible.
For a real decision, the relative costs of false admissions and missed admissions matter too. A model-selection report should connect the metric to those consequences rather than treating the largest accuracy number as the automatic choice.
Quick Recap
A practical way to compare classifiers
- Define the real prediction task. Identify the target, intended users, and consequences of each type of error before selecting a metric.
- Use representative data. A synthetic dataset can explain model behavior, but a deployment decision needs data that reflects the population and process where predictions will be used.
- Keep evaluation fair. Give candidate models the same held-out examples and apply the same preprocessing rules. Do not tune models against the test set.
- Compare generalization and error types. Review train and test scores, class-specific precision and recall, F1, and a confusion matrix rather than accuracy alone.
- Check stability. Evaluate across repeated splits or cross-validation and report score variation; one random split cannot show how results change with the sample.
- Choose for the use case. Consider performance, stability, interpretability, and the cost of errors together. A tiny score lead in one experiment is not a universal ranking.
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