Data Science Calculators
Confusion Matrix Calculator
Calculate accuracy, precision, recall, specificity, and F1 score from true/false positive and negative counts in a binary classifier.
Cosine Similarity Calculator
Calculate cosine similarity and the angle between two numeric vectors using the dot product and vector magnitude formula.
Data Sample Size Calculator
Calculate required survey sample size from confidence level, margin of error, and population size using Cochran's formula.
Euclidean Distance Calculator
Calculate straight-line Euclidean distance between two points of any dimension using the generalized Pythagorean formula.
F1 Score Calculator
Calculate F1 score as the harmonic mean of precision and recall, either from percentages directly or from confusion matrix counts.
Feature Scaling Calculator
Scale a list of values using min-max normalization or z-score standardization, the two standard feature scaling methods.
Mean Squared Error Calculator
Calculate mean squared error, RMSE, and MAE between actual and predicted value lists to evaluate regression model accuracy.
Precision Recall Calculator
Calculate precision and recall from true positive, false positive, and false negative counts using the standard classification formulas.
R-Squared Calculator
Calculate R-squared from actual and predicted value lists to see what fraction of variance a regression model explains.
ROC AUC Calculator
Calculate ROC AUC from positive and negative class scores using the Mann-Whitney rank-sum method, with an interpretation scale.
The Metrics That Decide If a Model Is Actually Good
A model's accuracy number can be misleading on its own — a classifier that always predicts "no fraud" can hit 99% accuracy on a dataset where fraud is rare, while being completely useless. These calculators work through the metrics data scientists actually rely on to judge model performance beyond raw accuracy: precision, recall, F1 score, R-squared, and sample size planning.
Popular Data Science Calculators
Ten tools span classification metrics, regression metrics, and study design:
- F1 Score Calculator — balances precision and recall into a single score, useful when class distribution is imbalanced.
- Precision Recall Calculator — computes both metrics from a confusion matrix to show false positive versus false negative tradeoffs.
- ROC AUC Calculator — measures a classifier's ability to distinguish classes across all decision thresholds.
- R-Squared Calculator — measures how much variance in a regression's outcome is explained by its inputs.
- Data Sample Size Calculator — finds the sample size needed for a study or experiment to detect an effect at a given confidence level.
Why Accuracy Alone Fails on Imbalanced Data
Accuracy treats every correct prediction equally, which breaks down badly when classes are imbalanced — a rare disease detector, a fraud model, or a spam filter can all achieve high accuracy by mostly predicting the majority class while missing nearly every case that actually matters. Precision and recall split accuracy into two more useful questions: of the positive predictions made, how many were correct (precision), and of the actual positives that existed, how many were caught (recall) — and F1 score exists specifically to combine both into a single number when neither alone tells the full story.
Frequently Asked Questions
When should I prioritize recall over precision?
When missing a positive case is more costly than a false alarm — medical screening and fraud detection typically favor recall, since a missed case is worse than a false positive that gets reviewed and dismissed.
What does an R-squared of 0.6 actually mean?
It means the model's inputs explain about 60% of the variance in the outcome variable, leaving 40% unexplained by the model — whether that's "good" depends heavily on the field and what's typically achievable with the available data.
Explore More
Working with model training itself? See the Machine Learning Calculators, or check the underlying statistics in the Statistics Calculators.