Accuracy is the least informative number you can report
A model that predicts the majority class every time can reach 95% accuracy on an imbalanced dataset while being useless. Machine learning coursework is largely assessed on whether you know that, and on what you report instead.
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Which metric, and why that one for this problem
Precision and recall trade against each other, and which matters depends entirely on what the errors cost. A screening model that misses cases is worse than one that produces false alarms; a spam filter is the other way round. Reporting the metric that suits the problem, and saying why, is the core of a good write-up.
The second issue is leakage, which is the most common serious error in student projects and often invisible in the results. Scaling before splitting, using future information in a time series, or tuning on the test set all produce excellent numbers that mean nothing. A report that explicitly describes the split and where preprocessing happened is doing something graders look for.
Then interpretation. What did the model actually learn, which features drove it, and where does it fail? A report that presents a confusion matrix and stops has described an output rather than analyzed a model.
- Metrics chosen for the cost of the errors in this problem
- The train, validation and test split described explicitly
- Leakage considered and ruled out
- Failure cases analyzed, not just headline performance
What we work on
Your own project and your own data
- Choosing evaluation metrics and justifying them
- Cross-validation, splits and avoiding leakage
- Handling class imbalance honestly
- Feature importance and model interpretation
- Writing the project report so the reasoning is visible
We explain. You build and train
- We do not write code or notebooks you will submit.
- We do not train models for you or produce results.
- We do not fabricate or alter data.
- We do not complete assignments or supply solutions.
- We work through your own project with you and review the write-up.
Reporting results you did not produce is fabrication rather than plagiarism, and it is treated more seriously. If the model did not work, the write-up explaining why is usually worth more points than people expect. Read the full policy.
Frequently Asked Questions
It depends on what the errors cost. Recall where missing a positive case is expensive, precision where false alarms are, F1 where you need a balance, and ROC AUC for ranking quality across thresholds. Accuracy alone is almost never sufficient, particularly with imbalanced classes.
Information from outside the training set influencing the model. Common causes are scaling or imputing before splitting, using features that would not exist at prediction time, and tuning hyperparameters on the test set. It produces excellent numbers and a model that fails in reality, and it is the most common serious flaw in student projects.
First, choose metrics that are not fooled by it. Then consider resampling, class weights, or threshold adjustment. Report what you did and show the effect. Resampling applied before the split is itself a leakage error, so the order matters.
No, and a careful analysis of why usually scores well. Diagnose whether it is underfitting, overfitting, insufficient data or an unlearnable problem, show the evidence, and say what you would try next. Reports that quietly present a weak model as a success are the worse outcome.
Enough to justify your choices. Why this algorithm suits this data, what it assumes, and what its limitations are for this problem. Explaining how the algorithm works in general terms, without connecting it to your data, is the padding version.
Send the notebook and the brief
We will check the split and the metrics, look for leakage, and tell you whether the write-up explains what the model actually learned.
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