Data Security
Data Security refers to the protection of digital data from unauthorized access, theft, corruption, or damage. It involves implementing various measures such as encryption, access controls, firewalls, and backups to ensure the confidentiality, integrity, and availability of data. Data Security is crucial in the field of Data Science and Artificial Intelligence as it involves handling sensitive and confidential information such as personal data, financial data, and intellectual property. Data breaches can result in significant financial losses, legal liabilities, and damage to reputation. Therefore, Data Scientists and AI experts must ensure that appropriate security measures are in place to protect data throughout its lifecycle, from collection to disposal.
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- False Positive Rate: False Positive Rate (FPR) is a statistical measure that represents the proportion of negative instances that are incorrectly classified as positive by a binary classification model. It is calculated as the ratio of false positives to the su ... Read More >>
- Intercept: In data science, intercept refers to the value at which the regression line crosses the y-axis. It is the value of the dependent variable when all independent variables are equal to zero. Intercept is an important parameter in linear regres ... Read More >>
- Model Retraining: Model retraining is the process of updating a machine learning model with new data to improve its accuracy and performance. As new data becomes available, the model is retrained to incorporate the new information and adjust its parameters. ... Read More >>
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