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There is no reliable recipe that moves every machine learning model from 80% to above 90% accuracy. An 80% score might reflect a flawed evaluation, an unsuitable metric, label or feature problems, or a genuinely difficult prediction task. The dependable approach is to find the bottleneck, change one thing at a time, and verify any improvement on data that did not guide model selection.
First, make sure the 80% score means what you think it means
Before changing algorithms or parameters, write down how the score was produced: which examples were used, how they were split, what metric was calculated, and whether those examples resemble the data the model will encounter in use. A score from training data is not a trustworthy estimate of performance on new cases. As the scikit-learn developers put it, “Learning the parameters of a prediction function and testing it on the same data is a methodological mistake” (cross-validation documentation).
Use training data to fit the model and a development procedure—such as cross-validation or a validation set—to make choices. Keep a final test set untouched until those choices are complete. Repeatedly checking that final set and selecting the model that scores best on it lets its results influence the process, making the final estimate optimistic.
Choose a split that reflects how the data arise
Random splitting is appropriate only when observations can reasonably be treated as independent and drawn from the same process. If several rows belong to the same person, device, or other group, use a group-aware split so related observations do not land on both sides. If the model will predict future observations, use a time-ordered split rather than allowing later data to inform predictions about earlier periods.
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For classification, stratification can preserve approximate class proportions across folds. It does not solve every sampling problem: scikit-learn cautions that stratification can make fold scores appear less variable than the underlying uncertainty. Select the split for the data structure, not simply because it produces a stable-looking score.
Rule out leakage in features and preprocessing
Leakage occurs when model development uses information that would not be available when making a real prediction. It can produce an impressive evaluation score that fails on novel production data. The scikit-learn developers define it this way: “Data leakage occurs when information that would not be available at prediction time is used when building the model” (common pitfalls and recommended practices).
Split the data first. Fit imputation, scaling, feature selection, and other learned transformations on training data only, then apply those fitted transformations to validation and test data. Do not fit them separately on the full dataset before splitting. A scikit-learn pipeline can keep transformations and the estimator together so that, during cross-validation and parameter search, each fold learns preprocessing only from its training portion.
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Also review whether every feature would actually exist at prediction time. A field recorded after the event being predicted, or a feature that indirectly encodes the label, can leak the answer even when the train/test split itself is correct.
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Accuracy is the fraction of predictions that are correct. It can hide poor performance on a rare class: a model that mostly predicts the common class may achieve a respectable aggregate score while missing many of the cases that matter. Compare the model with a simple dummy estimator and inspect outcomes by class before deciding that an accuracy increase is the right goal.
Balanced accuracy averages recall across classes, reducing the influence of class prevalence on the aggregate. Precision, recall, and other task-specific metrics may be more useful depending on the relative costs of false alarms and missed cases. There is no universally best metric; choose one that reflects the decision the model supports. The scikit-learn model-evaluation guide describes common metrics and their trade-offs.
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If decisions depend on predicted probabilities rather than only the selected class, evaluate probability quality separately. Calibration asks whether predictions assigned a probability correspond to that frequency of observed outcomes. Scikit-learn’s calibration guide uses probabilities near 0.8 as an explanatory example; that is not an accuracy result. Calibration may make probabilities more interpretable without increasing classification accuracy. Fit a calibrator using data independent of the base model’s training data.
Diagnose errors before trying more parameters
Once the evaluation design and target metric are sound, inspect where the model fails. A confusion matrix shows which classes are being mistaken for which others. Review representative false positives and false negatives, class frequencies, label consistency, missing values, and whether useful information is available among the features at prediction time.
These checks identify plausible next experiments, not guaranteed score increases. For example, correcting inconsistent labels may be more valuable than changing an estimator, while a genuinely ambiguous task may have a ceiling no parameter search can overcome. Establish a baseline and record the objective metric before comparing changes so that improvements are measured consistently.
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Run hyperparameter searches as controlled experiments
Hyperparameter tuning is useful only when the search is designed clearly. Define a reasoned parameter space, a cross-validation scheme suited to the data, and an explicit scoring metric. Grid search tests the combinations you supply; randomized search samples candidate combinations. If one score could conceal trade-offs, evaluate multiple metrics rather than optimizing an isolated number.
Keep the final test set out of the search. Compare candidate models using the same split strategy and objective, and report cross-validation mean and variability alongside the final held-out result. When alternatives are close, consider model complexity and training or inference cost as well as predictive performance. A more complicated model is not automatically better.
Scikit-learn documents a one-standard-error example that favors a simpler model whose score falls within one standard error of the best score. Treat this as a model-selection heuristic, not a rule that applies to every task. The relevant question is whether added complexity produces a meaningful, repeatable benefit for the intended use.
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Use learning and validation curves to choose what to try next
A learning curve compares training and validation performance as the amount of training data changes. It can help show whether additional examples are worth testing or whether the model appears limited in another way. More data can reduce variance in some settings, but it does not guarantee an accuracy increase.
A validation curve shows how training and validation scores change as a selected parameter varies. It can help distinguish a setting that is too simple from one that generalizes poorly, and guide the next experiment without blindly expanding a search. Interpret both curves in the context of the split, metric, and uncertainty; neither promises a ten-point gain.
What an improvement claim can—and cannot—say
The scikit-learn 1.9.1 cross-validation guide includes an illustrative linear SVM example on the Iris dataset with a reported held-out score of 0.96 after a particular train/test split. That is one dataset and one example split, not a general benchmark or evidence of a repeatable method for moving arbitrary models from 80% to over 90%.
No universal improvement is established for an unspecified model and dataset. A credible result states the task, data split, metric, and evaluation procedure, and reports whether the gain holds on data not used to choose the model. If the score does not improve under that test, the experiments have not demonstrated a reliable gain—even if development scores look better.
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