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...approach allows for the efficient processing of large datasets, as it does not require an immediate response to user inputs. ...rect user interaction. The model processes the data independently and does not require continuous user input.

...a significant investment of time or resources, or when the true labels are not directly observable. ...tly impact the performance of the resulting model. If the proxy labels are not sufficiently representative of the true labels, the model may fail to gener

...groups, demographic parity helps to ensure that machine learning models do not perpetuate or exacerbate existing societal biases. ..., it is not without its limitations. For instance, demographic parity does not necessarily guarantee equal accuracy rates for different demographic groups

...lead to a model that performs poorly in real-world applications, as it is not able to generalize well to the broader population. In this article, we will ...f sampling bias that can occur in machine learning. These include, but are not limited to:

[[Static models]] are machine learning models that do not change or adapt after they have been trained on a dataset. Once a static mo ...y: Static models are often simpler to understand and implement, as they do not require complex update mechanisms or continuous learning.

...s context, an imbalanced dataset refers to a dataset where the classes are not represented equally. This can lead to poor performance for certain machine ...undersampling technique that combines both Tomek Links and the [[Wilson's Edited Nearest Neighbor]] (ENN) rule. The method involves removing majority class

...s that occurs in machine learning when the data used to train a model does not accurately represent the target population or the problem space. This leads ...y a subset of the population data may be available for training, which may not accurately represent the entire population. This can lead to a model that i

...s an action uniformly at random from the set of available actions. It does not take into account the current state of the environment or the potential con ...able to outperform a random policy, it may indicate that the algorithm is not learning effectively or that there is an issue with the problem formulation

...l to the product of their individual probabilities. If the data points are not independent, their relationships may introduce bias into the model and affe ...], [[k-means clustering]], and [[neural networks]]. If the data points are not identically distributed, the model may have difficulty in identifying the u

...lgorithmic discrimination]], even when the original sensitive attribute is not explicitly used in the model. It is important for researchers and practitio ...se pieces of information are called "proxy variables" for the thing you're not allowed to know, like someone's race, gender, or age. Even if you don't use

In the context of machine learning, the term "root directory" does not directly refer to a specific concept or technique. Instead, it is related t While root directories are not a specific machine learning concept, they play an essential role in organiz

...rld problems, and if the relationship is more complex, linear models might not provide accurate predictions. ...pendent of each other. This means that the error at one observation should not affect the error at another observation. If this assumption is violated, it

...antage of L1 regularization is its ability to produce sparse models, which not only helps in mitigating overfitting but also improves the interpretability ...hich can lead to suboptimal solutions. Additionally, L1 regularization may not perform well in cases where all features are equally important or contribut

...h complex, nonlinear relationships, or where the underlying assumptions do not hold. ...times real-life situations are more complicated, and a straight line might not be the best way to describe them.

...ine learning that occurs when the training data used to develop a model is not representative of the population of interest. This can lead to a model that ...process is based on convenience, accessibility, or other factors that may not be related to the phenomenon being studied.

* They are generally more flexible, as they do not require assumptions about the underlying distribution of the data. * Discriminative models cannot generate new samples, as they do not model the joint probability distribution of the input features and class la

...crease as expected or shows sudden spikes, it may signal that the model is not generalizing well to the data. ..., if it's learning too much and forgetting the important stuff, or if it's not learning enough. By looking at the loss curve, they can change some things

...to the forward propagation or backpropagation steps, and their weights are not updated during that iteration. ...th probability 'p'. After training, during the inference phase, dropout is not applied, and the output of each neuron is scaled by a factor of '1-p' to ac

...ess is essential to ensure that algorithmic decisions are equitable and do not discriminate against particular groups. This article focuses on the incompa ...veral fairness metrics have been proposed in the literature, including but not limited to:

...tions. The brevity penalty helps ensure that the generated translations do not merely consist of short, high-precision phrases. ...ric that does not account for the meaning of the text. As a result, it may not always correlate with human judgments of translation quality.

...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

...urate than other multi-class strategies, particularly when the classes are not linearly separable. * The approach may be less interpretable, as the classifiers do not directly provide information about pairwise relationships between classes.

...f the story. When this happens, the computer might make decisions that are not fair to everyone. To fix this, we can try to give the computer examples fro [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...e utilized in a wide range of machine learning applications, including but not limited to: [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...hypotheses or beliefs. For example, they may choose a [[dataset]] that is not representative of the population or sample, or they may apply preprocessing ...have performed well in previous experiments, even if these algorithms may not be the best fit for the current problem. Similarly, during the parameter tu

...orting, and serving machine learning models. It is designed to encapsulate not only the model's architecture and weights but also the computation graph, m ...more easily, no matter what programming language or platform they use. It not only has the model's structure and important parts but also includes any ex

...wide range of tasks, including machine learning algorithms. While CPUs may not be as fast or efficient as specialized hardware for machine learning, they ...sses. CPUs are like a Swiss Army knife - they can do many things but might not be the best at everything. GPUs are like a big team of workers who can all

Prediction bias can arise from several sources, including but not limited to: ...h as linearity, normality, or homoscedasticity. When these assumptions are not met, the model may produce biased predictions. For instance, a linear regre

...ationary state is one in which the underlying data-generating process does not change over time. Non-stationary states can make learning more difficult, a ...perty, meaning that the future state depends only on the current state and not on previous states. This property simplifies learning and inference in many

...inear. This may not always hold true, and in such cases, linear models may not perform well. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ore uniform representation of the input data. However, average pooling may not be as effective as max pooling in preserving high-frequency features or edg [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...mething right. However, sometimes you might miss a red ball and think it's not red when it actually is. This is called a false negative, and the false neg [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...se too many complicated pieces, the car might work great on the carpet but not on the sidewalk. That's like overfitting in machine learning. ...is way, your car will be simpler and work better on all surfaces. It might not be perfect on any one surface, but it will work well enough on all of them.

...tructures can be limiting in some scenarios, particularly when the data is not well-distributed along the axes, or when the underlying structure of the da ...or or shape. This makes it easier to organize the toys, but sometimes it's not the best way to do it, because the toys might have other important features

..., where the future state of a system depends only on its current state and not on its previous history. ...l. It's like having a short memory, only remembering where you are now and not worrying about the past.

...r the predicted or ground truth bounding boxes (or segmentation masks) but not both. However, IoU has some limitations. For example, it does not account for the quality of the predicted class labels or the number of fals

...at leads individuals to perceive members of an out-group, or those that do not belong to their own social or cultural group, as more similar to one anothe [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...training dataset increases the likelihood of overfitting, as the model may not have enough information to learn the underlying patterns in the data. This ...different techniques to help the model focus on the important patterns and not get distracted by the small, unimportant details.

...deep learning model) is good at building complicated structures but might not always connect all the different bricks well. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...emoval: Remove common words, such as "a," "an," "the," and "is," which may not hold significant meaning in the context of the given problem. 2. Semantics: BoW does not take into account word meanings and semantic relationships between words.

...ange of numbers will be smaller, like between 25 and 28 points. If they're not so sure, the range will be bigger, like between 15 and 35 points. This way, [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ning to the end. These models, in contrast to [[bidirectional models]], do not possess the ability to consider information from later portions of the inpu ...NNs that only process input sequences in a forward manner, meaning they do not have any mechanism to incorporate information from later parts of the input

...ocess performed on a separate dataset, called the validation set, which is not used during training. The validation step helps to monitor the model's perf ...al evaluation of a machine learning model, conducted on a dataset that has not been used during training or validation. This step aims to provide an unbia

...s to practice with. To make sure you're really good at solving puzzles and not just memorizing the answers to the ones you practiced, your teacher keeps s ...s make sure the program is good at solving different kinds of problems and not just the ones it practiced with.

...Bias refers to the error caused by using a simplified hypothesis that does not capture the true relationship between the input and output variables. Varia However, sometimes the best tool for those few toys might not work well for other broken toys you haven't tested yet. In machine learning

...refers to the category or label assigned to instances in a dataset that do not possess the characteristics or features of interest. It is the counterpart [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...y time shift. In other words, the statistical properties of the process do not change for any time shift, implying that the process maintains the same beh ...between any two-time points depends only on the time lag between them, and not on the actual time at which the covariance is computed.

...used for validation and testing. This technique ensures that the model is not exposed to future data during the training process, thus preserving the tem [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...re the sample data used to train or evaluate a machine learning model does not accurately represent the underlying population or the target domain. This i ...ve of the population, the model may learn patterns or associations that do not generalize well to new data points. Examples of non-random sampling include

...rocks. You have a tool that helps you identify the special rocks, but it's not always correct. The PR AUC is a number that tells you how good your tool is [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ar, R., Mahowald, M. A., Douglas, R. J., & Seung, H. S.]]. However, it was not until the 2012 publication of the groundbreaking paper by [[Alex Krizhevsky [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...paper airplane. You have a lot of different folds you can make, and you're not sure which combination will make the best airplane. You start by making a r [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...mpling without replacement]] is a technique where a sample, once drawn, is not returned to the population, thus making it ineligible for further selection ..., sometimes it can cause the model to focus too much on a few examples and not learn as well from the others.

...h occurs when a model learns to perform well on the training data but does not generalize well to unseen data. Regularization works by adding a penalty te [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...mple, if a dataset over-represents a particular demographic, the model may not generalize well to other groups. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...RMSE signifies a poorer fit. However, it should be noted that the RMSE is not a normalized metric and is dependent on the scale of the target variable. T ...s the model is better at predicting things, while a higher RMSE means it's not as good.

...p you figure out if other, more complicated ways of guessing are better or not. If your friend uses a fancy method to guess the number of jellybeans and g [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...demonstrates that single-layer perceptrons cannot solve problems that are not linearly separable. This issue can be addressed by using multi-layer percep ...ing out if an animal is a mammal, a reptile, or a bird, a perceptron might not be able to do it. That's when we use more advanced methods, like a multi-la

...ses a [[lazy execution]] strategy, meaning that the nodes in the graph are not executed immediately when defined. Instead, the execution is deferred until [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...iables constant [[1]](#ref1). This means that the model's decisions should not depend on the sensitive attribute when other factors are held constant. ...ions [[3]](#ref3). This approach can help ensure that the final model does not rely on the sensitive attributes when making decisions, leading to counterf

...VI is its applicability to a wide range of models, including those that do not provide intrinsic feature importance measures, such as [[random forests]] a ...e importance of highly correlated features, as the permutation process may not significantly impact the model's performance when other correlated features

...imilar: it helps us figure out if our model is good at making predictions, not just memorizing the data. By testing the model on different parts of the da [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...duction]], and [[density estimation]]. Although unsupervised learning does not produce predictions in the same sense as supervised learning, the discovere [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

* The user must specify the number of clusters (k) beforehand, which may not always be known or easily determined. * K-means assumes that clusters are spherical and equally sized, which may not be true for some datasets.

...information in the data. Furthermore, if a subset of features selected is not representative of the overall distribution of features in the dataset, perf ...[Category:Machine learning terms]] [[Category:not updated]] [[Category:Not Edited]]

...nd to evaluate its performance. In unsupervised learning tasks, labels are not provided. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...the process of simplifying the decision tree by removing branches that do not significantly contribute to the model's performance. This can help reduce t * '''Minimal data preprocessing''': Decision trees do not require feature scaling or normalization and can handle missing data and ca

...ir content and sentiment. These methods can be useful when labeled data is not available, but they may be less accurate than supervised learning approache [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

4. Repeat steps 2 and 3 until convergence is reached, i.e., the centroids do not change significantly or a predetermined number of iterations have been perf [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...are in the bag. You make some guesses based on what you see, but you might not be very accurate. The calibration layer in machine learning is like a frien [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ant alternatives (IIA): The odds of choosing one category over another are not affected by the presence or absence of other categories. However, these assumptions may not always hold, and violations can lead to biased or inefficient parameter est

...e learning models to ensure that the predictions made by the algorithms do not disproportionately disadvantage or benefit specific groups of individuals, [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...can help identify and address disparate treatment of individuals that may not be captured by group-level fairness measures. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...when the relationship between the predictors and the response variable is not constant across the distribution or when the data exhibits heteroskedastici [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...contributes equally to the model's predictions. However, normalization may not always be necessary or beneficial, particularly when the input features are [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...th the target variable. These methods are computationally efficient and do not rely on any specific machine learning model. Examples of filter methods inc ...re complex relationships between features and can reveal patterns that are not visible in the linear space. Examples of nonlinear techniques include t-Dis

In the game, you might not know the best moves right away. So, you use a technique called Q-Learning t [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

* '''Limited adaptability''': Once trained, offline models may not adapt well to new data or changing patterns without retraining on an update [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...[Category:Machine learning terms]] [[Category:not updated]] [[Category:Not Edited]] ...more, agglomerative clustering can be applied with any distance metric and not just specific types of data.

In unsupervised novelty detection, the algorithm does not rely on labeled data and instead learns the underlying structure or distrib ...similar data points together and identify novel patterns as those that do not belong to any cluster.

...for receiving and processing data from external sources. They typically do not have an activation function, as they directly transmit the input data to th [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...he future system dynamics depend only on the current state and action, and not on the history of previous states and actions. ...predictable, so when you choose an action, there's a chance that you might not end up where you expect. Sometimes, you get points or rewards for doing wel

...ors, which are then used to predict the preferences for items the user has not interacted with yet. Item-based collaborative filtering, on the other hand, [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...d make predictions or decisions. Sometimes, the information in the data is not very clear or easy for the model to understand. To help the model learn bet [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

# '''Cyclicality''': Fluctuations in the data that are not periodic, but occur over irregular intervals. # '''Noise''': Random variations in the data that are not attributable to any specific trend, seasonality, or cyclicality.

...ferent ways. GANs have a friend who tells them if their work looks real or not, and they keep improving until their friend can't tell the difference. VAEs [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...nce of incoming data. A value close to 0 indicates that the information is not important, while a value close to 1 signifies that the information is cruci ...ngs to keep. If something is important, it will open wide to let it in. If not, it will stay closed.

...ven get confused, just like how a decision tree can become too complex and not work well on new information. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

While translational invariance is a powerful property, it is not without challenges and limitations. One such limitation is the lack of rota [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...retations. However, in the context of machine learning, crash blossom does not have a direct application or meaning. Nevertheless, we can discuss related [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ses, accuracy or the [[receiver operating characteristic (ROC) curve]] may not provide a comprehensive assessment of model performance. The precision-reca [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...n the overall structure and behavior of a machine learning model. They are not learned during the training process, but rather are set by the practitioner [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...focus on exploitation, selecting actions with known high rewards, and may not adequately explore the environment to discover better actions. * Local optima: The greedy approach may get stuck in local optima, as it does not consider potential improvements that might be achieved by selecting actions

...ems. Examples of categorical targets include classifying emails as spam or not spam, determining whether an image contains a cat or a dog, and identifying [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

[[Unsupervised learning]] models do not rely on labeled data, but rather they aim to identify patterns or structure [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...gnize shapes at the same time, and checks that the toy is working well and not making mistakes. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

* '''[[Undirected graph]]''': A graph where the edges do not have a direction, indicating a symmetric relationship between the connected * '''[[Unweighted graph]]''': A graph in which edges do not have associated weights, and all connections between nodes are considered e

...and makes it better at guessing. Sometimes, in a guessing game, there are not many examples of an important object, so it's harder to learn about it. Upw [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...g the performance of a machine learning model requires a separate dataset, not used during training, to ensure an unbiased assessment. Various validation * [[Hyperparameter Tuning]]: The optimization of model parameters that are not learned during training, which can significantly impact performance.

* Performance ceiling: Although the algorithm is robust and accurate, it may not always outperform other state-of-the-art machine learning techniques like [ [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

GLMs have a wide range of applications across various fields, including but not limited to economics, biology, social sciences, and engineering. Some commo [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ictive rate parity is important for ensuring that a model is fair and does not discriminate against any particular group. ...est. This helps make sure that the teacher is treating everyone fairly and not favoring one group over another.

...ight have unfair patterns in it, or because the program's design itself is not fair. To fix this problem, scientists work on making the computer programs [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...be more confident that it has learned how to recognize shapes in general, not just the ones it saw during training. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...he game. It helps the agent figure out if the choices it made were good or not. The agent tries to learn how to make better decisions so that it can get a [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

- explicitlyRequestedByUserDiagramLanguage is optional, if not specified, default 'mermaid' is used. ...the /explore-diagrams endpoint nor /show-ideas endpoint when the user does not use their respective key phrases

.... You have a machine that can pick apples and tell you if they're green or not. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...e into account additional features, context, or user preferences that were not considered by the primary model. This secondary model can be based on vario [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...erparts. As they only take into account one direction of context, they may not fully capture the meaning of a given word or phrase. Additionally, unidirec [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...hen the data being used to train a model is influenced by factors that are not representative of the true underlying phenomenon. These factors can lead to ...election can lead to reporting bias when the data used to train a model is not representative of the target population. This may occur if the data is coll

* It does not perform variable selection, which may result in suboptimal models when ther [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...widespread use, MNIST has faced criticism for being too simplistic and for not adequately representing real-world challenges in the field of computer visi [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...prove model performance by capturing complex patterns in the data that may not be apparent to the original set of features. ...techniques help ensure that the model generalizes well to new data and is not overfitting the training data.

...hin the margin, while instances correctly classified outside the margin do not contribute to the loss. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

Unsupervised methods of feature extraction do not require any prior knowledge or labels associated with the input data. These Feature extraction is widely applied in various domains, including but not limited to:

...f items on which all raters agree. While easy to compute, this method does not take into account the possibility of agreement occurring by chance. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

While L1 loss is not as computationally efficient as some other loss functions like MSE, it is r [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...asier to interpret as they represent actual data points, whereas means may not correspond to any real-world observation. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

There are various types of sequence models in machine learning, including but not limited to: [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...in various machine learning and deep learning applications, including but not limited to: [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

[[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...rning, the reward helps the robot (agent) know if it's doing a good job or not, and it helps the robot decide which actions to take. The robot needs to tr [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

* Model complexity: Fine tuning may not always improve performance for simpler models or tasks, where the pre-train [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...techniques that employ a practical approach to finding an adequate, though not always optimal, solution to complex problems. In [[machine learning]], heur [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

* May introduce sampling bias if the subsampling method does not properly represent the overall distribution and characteristics of the orig [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...he proportion of positive outcomes is the same for each group, but it does not account for differences in the underlying distribution of the data, which m [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...in a decision tree, representing an outcome or class label. Leaf nodes do not have any outgoing edges, and they are where the decision-making process end [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...odel. These representations are learned during the training process and do not require expert knowledge. Deep learning models, such as [[Convolutional Neu [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

* It does not require specifying the number of clusters a priori, as the dendrogram can b [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...he model to produce well-calibrated probability estimates, as it penalizes not only incorrect predictions but also the degree of confidence in those predi [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ave been employed in a wide range of machine learning tasks, including but not limited to: [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

Agents have numerous applications in machine learning, such as but not limited to: ...[Category:Machine learning terms]] [[Category:not updated]] [[Category:Not Edited]]

* May not be the most efficient exploration strategy for all problems. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...fix any issues you might encounter while building the tower, but it might not be as fast or efficient as following a pre-planned design. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...vised learning]] algorithms in scikit-learn are designed for tasks that do not have labeled data, including: [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

In asynchronous updates, worker nodes do not wait for the gradients to be aggregated before proceeding with the next ite [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...milarity measures are sensitive to the scale of the data, while others are not. It is important to select a measure that is appropriate for the given data [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ion, and test sets based on their timestamps, ensuring that future data is not used to predict past events. This can help prevent data leakage and provide [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...s if certain features correlate more strongly with specific groups and are not equally representative of all groups in the dataset. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...is much smaller than its number of negative instances. Accuracy alone may not be sufficient as accuracy only requires predicting the majority class accur ...possess a magical machine that can tell you if a picture contains a cat or not. Unfortunately, sometimes the machine might make an error and indicate ther

...nformation that isn't fair to everyone. This can make the computer program not fair, too. To fix this, people who create computer programs can use special [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...you know and keep learning. You keep doing this until you feel like you're not getting any better at recognizing animals. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...[Category:Machine learning terms]] [[Category:not updated]] [[Category:Not Edited]]

...function achieves a minimum value relative to the neighboring points, but not necessarily the global minimum. Non-convex loss surfaces can have multiple [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

[[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ation. Clustering is an unsupervised learning method, meaning that it does not rely on pre-labeled data for training but rather discovers inherent relatio [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...them particularly suitable for applications in which the optimal action is not immediately apparent and must be learned through exploration. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...create pure splits in the decision tree. Additionally, Gini importance may not accurately reflect the importance of features in the presence of correlated [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...earning models can be designed to handle various modalities, including but not limited to: [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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1. In a decision tree for classifying whether an email is spam or not, a condition might be "if the number of words in the subject line is greate [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ne to overfitting if the number of iterations or the depth of the trees is not tuned properly. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...set of samples. This can lead to the generated samples lacking variety and not accurately representing the target distribution. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...a similar manner. The computer is given a task to do, and at first it may not do it very well. But with each subsequent assignment, AdaGrad helps it lear ...[Category:Machine learning terms]] [[Category:not updated]] [[Category:Not Edited]]

...ficial General Intelligence (AGI) is like being gifted in multiple areas - not just one. Imagine that you excel at playing with toys but also drawing, run ...a robot that is intelligent and capable of doing multiple things at once - not just one! It could play games, draw pictures, run fast and even sing! That

...of an outcome based on a set of input features. However, these methods do not directly address the question of how the outcome would change if a particul [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

As the Transformer architecture does not have any inherent notion of the order of the input elements, '''positional [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...f the environment depends only on the current state and the chosen action, not on the history of previous states or actions. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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* It assumes that the target function is smooth, which may not be the case for all optimization problems. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...the other hand, if you have too few people (a narrow network), they might not be able to solve the puzzle at all because they can't figure out all the di [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...Processing Units ([[CPU]]s) and Graphics Processing Units ([[GPU]]s) were not optimized for the unique workload characteristics of deep learning, leading [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...what happened: how you balanced, how you pedaled, and whether you fell or not. Experience Replay in machine learning is like keeping a scrapbook of all y [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...come up with faster ways to weigh the candies, but these faster ways might not be as accurate as the full softmax (magical scale). [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...based tokenization may face challenges when dealing with languages that do not use spaces between words or when handling morphologically rich languages, w [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ngful gradients even when the generator and discriminator distributions do not overlap, reducing the likelihood of vanishing gradients. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

Meta-learning has a wide range of applications, including but not limited to: [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...may result in a loss of important information if valuable data points are not included in the reduced dataset. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...ving the robot a way to say, "I think this decision might be good, but I'm not sure. There's [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...o a situation where the output or target variable of a predictive model is not restricted to two distinct classes or labels. This contrasts with binary cl [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...esources. This approach can lead to faster training times, as resources do not need to wait for slower counterparts. However, it may also result in less a [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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Imagine you have a team of people who are not very good at solving a specific problem, but they are all slightly better t [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...[Graphics Processing Unit (GPU)]] or [[Tensor Processing Unit (TPU)]], may not be sufficient to store the entire model and its associated data. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

* '''Sequence data and recurrent networks:''' BN may not be well-suited for sequence data or recurrent neural networks (RNNs), where [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...t noise is the type of noise that is present within the data itself and is not a result of the data collection process. This type of noise is usually due [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...and infeasible for some domains. Moreover, supervised learning models may not generalize well to new, unseen data, as they are often biased towards the s [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

...and you're trying to learn how much force to use at each level. If you're not careful, the force you use at the bottom can affect the top, and if you use [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...build something cool. The '''Dataset API (tf.data)''' is like a helper who not only brings you the LEGO pieces you need but also sorts and organizes them [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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Unstructured data, on the other hand, does not follow any specific schema or format. It may include text, images, audio, v [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...er is typically composed of a set of neurons, each of which receives input not only from the previous layer but also from its own output at the previous t [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...addressed by using content-based filtering or hybrid approaches, which do not rely solely on user behavior data. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...earning is particularly useful in situations where the optimal solution is not known beforehand, and the agent must learn through trial and error. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...s, language models can generate more accurate predictions, as they capture not only the immediate context ([[bigram]]) but also a broader context of the s Trigram language models can suffer from data sparsity, as not all possible combinations of three words may be present in the training dat

...lacement, which means that some instances may be repeated while others may not be included in the sample. Next, a decision tree is grown using the selecte [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...lenge in using AR is making these objects appear real even though they are not physically present. ...[Category:Machine learning terms]] [[Category:not updated]] [[Category:Not Edited]]

...lity of natural language, as they required extensive manual work and could not adapt to new linguistic data. [[Category:Terms]] [[Category:Machine learning terms]] [[Category:Not Edited]] [[Category:updated]]

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...put data. This occurs when the model overfits to the training data or does not generalize well to new or unseen data. This behavior has been observed in v ...er. To help the model learn better, we can show it more examples, teach it not to focus on small details too much, or combine what it learns with other mo

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...with the celebration, and appropriately-placed shadows to images that did not mention them." <ref name="”2”"></ref> Furthermore, DALL-E can expand im However, the program is not without its limitations. For example, it has difficulties distinguishing "A

...ration|generate text]] based on [[natural language input]]s ([[prompts]]). Not only does it create human-like text but also is able to produce code or oth ...arizing reports or creating content that can then be manually reviewed and edited. <ref name="”8”"> Zhang, M and Li, J (2021). A commentary of GPT-3 in M

...Preview_commit is meant for you, the AI assistant to check your work, and not the end user who is trying to creatively instruct the overall design workfl ...s logged is something about an illegal return statement at line X, this is not to suggest you are meant to DELETE the return statement in a subsequent act