Performance

Performance in Data Science refers to the efficiency and effectiveness of a model or system in achieving its intended goals. It is a measure of how well a model or system is able to process and analyze data, make predictions or classifications, and provide insights or recommendations. Performance can be evaluated using various metrics such as accuracy, precision, recall, F1 score, AUC-ROC, and others depending on the type of problem and the nature of the data. Improving performance often involves optimizing the model's parameters, selecting appropriate features, reducing overfitting, and increasing the amount and quality of data. Performance can also be affected by external factors such as hardware limitations, data quality, and user behavior.
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  • Random Sampling: Random sampling is a statistical technique used in data science to select a subset of data points from a larger dataset. The selection of data points is done randomly, without any bias or preference towards any particular data point. Random ... Read More >>
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