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...n. This concept is essential for ensuring that machine learning systems do not discriminate against or favor specific groups of individuals. ...' When the distribution of classes or demographic groups in the dataset is not equal, it may lead to biased models and hinder achieving predictive parity.

...erformance metrics, such as accuracy, can be misleading, and the model may not generalize well to unseen data. In order to address this issue, a variety o ...e the risk of overfitting compared to random oversampling, as the model is not solely reliant on duplicated samples.

...the probability of an event occurring (p) to the probability of the event not occurring (1-p). In other words, the log-odds represents the natural logari ...can help predict if something will happen or not (like if it will rain or not) based on what you know.

...it may lead to overfitting or underfitting if the number of iterations is not chosen carefully. ...ory usage or execution time. This approach ensures that the algorithm does not consume excessive resources, but it may lead to suboptimal solutions if the

...cted behavior within a specific context or environment. These outliers may not necessarily be anomalous in other contexts or when considered in isolation. ...of data points that together exhibit abnormal behavior. These outliers are not necessarily anomalous individually, but their collective behavior deviates

Unlike probability sampling methods, convenience sampling does not rely on randomization. Instead, researchers select the sample based on its ...non-random nature, convenience sampling often results in samples that may not be representative of the overall population. This can lead to biased result

...etween categories: The binary representation used in one-hot encoding does not capture any inherent relationship between categories, which may exist in th ...to sparse matrices, where the majority of the elements are zeros. This may not be efficient for some machine learning algorithms.

...a common problem where a model performs well on the training data but does not generalize well to new, unseen data. The regularization rate, also known as ...mpler, which can lead to underfitting, while a low regularization rate may not provide enough constraint, leading to overfitting. The optimal regularizati

...ea behind OOB evaluation is to use a portion of the training data that was not used during the construction of individual base learners, for the purpose o ...aning that some instances may be selected more than once, while others may not be selected at all. Consequently, a portion of the training data, known as

...n performance, as the model's predictions may be systematically biased and not applicable to the population at large. ...population, attrition during longitudinal studies, or participants simply not responding to surveys or other data collection efforts.

...ing model that is too complex and only works well on the training data but not on new data. ...nce between being good at the task (throwing the ball into the basket) and not being too specific to the backyard (keeping the model simple). This way, th

...e decisions based on certain conditions. These operations include AND, OR, NOT, and XOR. They are often used in [[decision tree]] learning algorithms and ...sed on the comparison. Common relational operations include equal to (==), not equal to (!=), less than (<), greater than (>), less than or equal to (<=),

...ses an inherent order or ranking, but the intervals between the values are not necessarily consistent or meaningful. This unique characteristic of ordinal ...hat can be ranked or ordered, but the differences between those values are not necessarily quantifiable or meaningful. The data can be represented by a se

...model that fails to capture the complexity of the data and therefore does not perform well on new data. ...of green apples while teaching it, but later it sees red apples, it might not recognize them as apples because it hasn't seen that type before.

NaN trap, short for 'Not a Number' trap, is a common issue encountered in machine learning algorithm ...e can help to ensure that the optimization process remains stable and does not generate NaN values. Adaptive learning rate algorithms, such as AdaGrad, RM

...ns. This helps the computer make fair choices for everyone. But sometimes, not knowing these things can also make it harder for the computer to do its job [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ned dimensions of the input space. However, such orthogonal boundaries may not be suitable for all types of data, especially when the underlying structure ...n be computationally expensive to compute and may result in overfitting if not properly regularized. Additionally, they may be more sensitive to noise or

..., the sample size should be large enough to guarantee accurate results but not so large that it becomes impractical or time-consuming. Furthermore, the le ...as external events or seasonal fluctuations. Furthermore, A/B testing may not be suitable for testing complex changes like those to user workflows or pro

...ization, larger lambda values generally result in smaller coefficients but not necessarily zero coefficients. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...structures for efficient handling of large and complex datasets. Although not specifically designed for machine learning, it has become an essential tool ...work with a lot of information (like numbers and words) more easily. It's not specifically for machine learning, which is like teaching computers to lear