A/B testing is the validation of the 2 hypotheses on the data – usually on the real data. Then, the hypothesis with the better result (lower error of the estimation) is chosen as an estimator for future data.
- Preface
- Classification Using K Nearest Neighbors
- Naive Bayes
- Decision Trees
- Random Forest
- Clustering into K Clusters
- Regression
- Time Series Analysis
- Statistics
- R Reference
- Python Reference
- Glossary of Algorithms and Methods in Data Science
A/B Testing
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