_id stringlengths 2 6 | text stringlengths 3 395 | title stringclasses 1 value |
|---|---|---|
C1500 | 1:146:07Suggested clip · 120 secondsUnivariate analysis SPSS - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C1501 | In mathematics, a Markov decision process (MDP) is a discrete-time stochastic control process. MDPs are useful for studying optimization problems solved via dynamic programming and reinforcement learning. | |
C1502 | Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. | |
C1503 | Results: Within a given review, a change in prevalence from the lowest to highest value resulted in a corresponding change in sensitivity or specificity from 0 to 40 percentage points. Overall, specificity tended to be lower with higher disease prevalence; there was no such systematic effect for sensitivity. | |
C1504 | Shift-invariance: this means that if we shift the input in time (or shift the entries in a vector) then the output is shifted by the same amount. | |
C1505 | Stepwise regression is an appropriate analysis when you have many variables and you're interested in identifying a useful subset of the predictors. In Minitab, the standard stepwise regression procedure both adds and removes predictors one at a time. | |
C1506 | With supervised learning, you have features and labels. The features are the descriptive attributes, and the label is what you're attempting to predict or forecast. | |
C1507 | Key Differences between AI, ML, and NLP ML is an application of AI. Machine Learning is basically the ability of a system to learn by itself without being explicitly programmed. Deep Learning is a part of Machine Learning which is applied to larger data-sets and based on ANN (Artificial Neural Networks). | |
C1508 | Cluster sampling refers to a type of sampling method . With cluster sampling, the researcher divides the population into separate groups, called clusters. For example, given equal sample sizes, cluster sampling usually provides less precision than either simple random sampling or stratified sampling. | |
C1509 | A sample may be selected from a population through a number of ways, one of which is the stratified random sampling method. A stratified random sampling involves dividing the entire population into homogeneous groups called strata (plural for stratum). Random samples are then selected from each stratum. | |
C1510 | Maximum likelihood estimation involves defining a likelihood function for calculating the conditional probability of observing the data sample given a probability distribution and distribution parameters. This approach can be used to search a space of possible distributions and parameters. | |
C1511 | In statistics, an F-test of equality of variances is a test for the null hypothesis that two normal populations have the same variance. | |
C1512 | A problem is an issue you can resolve while a constraint is an issue you cannot resolve. That is the simplest definition of these two terms. You can also define it in terms of your control over the situation. A problem is an issue where you have control over while a constraint is one where you do not have control over. | |
C1513 | Rejecting the null hypothesis when it is in fact true is called a Type I error. When a hypothesis test results in a p-value that is less than the significance level, the result of the hypothesis test is called statistically significant. Common mistake: Confusing statistical significance and practical significance. | |
C1514 | Essentially, multivariate analysis is a tool to find patterns and relationships between several variables simultaneously. It lets us predict the effect a change in one variable will have on other variables. | |
C1515 | Benefits of Usability TestingUsability testing provides an unbiased, accurate, and direct examination of your product or website's user experience. Usability testing is convenient. Usability testing can tell you what your users do on your site or product and why they take these actions.More items• | |
C1516 | Odds ratios are one of those concepts in statistics that are just really hard to wrap your head around. For example, in logistic regression the odds ratio represents the constant effect of a predictor X, on the likelihood that one outcome will occur. The key phrase here is constant effect. | |
C1517 | Weight is the parameter within a neural network that transforms input data within the network's hidden layers. A neural network is a series of nodes, or neurons. Within each node is a set of inputs, weight, and a bias value. Often the weights of a neural network are contained within the hidden layers of the network. | |
C1518 | The Monty Hall problem is one of those rare curiosities – a mathematical problem that has made the front pages of national news. Everyone now knows, or thinks they know, the answer but a realistic look at the problem demonstrates that the standard mathematician's answer is wrong. | |
C1519 | We say that X and Y are independent if P(X=x,Y=y)=P(X=x)P(Y=y), for all x,y. Intuitively, two random variables X and Y are independent if knowing the value of one of them does not change the probabilities for the other one. In other words, if X and Y are independent, we can write P(Y=y|X=x)=P(Y=y), for all x,y. | |
C1520 | Describe the scores in such a sample. If the standard deviation is 0 then the variance is 0 and the mean of the squared deviation scores must be 0. Thus, when the standard deviation equals 0, all the scores are identical and equal to the mean. | |
C1521 | The negative binomial distribution is a probability distribution that is used with discrete random variables. This type of distribution concerns the number of trials that must occur in order to have a predetermined number of successes. | |
C1522 | In General, A Discriminative model models the decision boundary between the classes. A Generative Model explicitly models the actual distribution of each class. A Discriminative model learns the conditional probability distribution p(y|x). Both of these models were generally used in supervised learning problems. | |
C1523 | Optimization falls in this category — given an optimization problem, you can, in principle, find a solution to the problem, without any ambiguity whatsoever. Machine learning, on the other hand, falls in the domain of engineering. Problems in engineering are often not mathematically well-defined. | |
C1524 | Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself. | |
C1525 | ·2 min read Here is a comparison of three basic pooling methods that are widely used. The three types of pooling operations are: Max pooling: The maximum pixel value of the batch is selected. Average pooling: The average value of all the pixels in the batch is selected. | |
C1526 | Hinge loss simplifies the mathematics needed for SVM thus leading to computational effective results while maximazing the error. If you need real time decisions with a lesser accuracy depend on it. Cross entropy is one of ancestor probabilistic decision making that minimizes the error but computationally ineffective. | |
C1527 | Backward chaining is known as goal-driven technique as we start from the goal and divide into sub-goal to extract the facts. Backward chaining is suitable for diagnostic, prescription, and debugging application. 7. Forward chaining can generate an infinite number of possible conclusions. | |
C1528 | The short answer is "no"--there is no unbiased estimator of the population standard deviation (even though the sample variance is unbiased). However, for certain distributions there are correction factors that, when multiplied by the sample standard deviation, give you an unbiased estimator. | |
C1529 | BioInformatics – This is one of the most well-known applications of Supervised Learning because most of us use it in our day-to-day lives. BioInformatics is the storage of Biological Information of us humans such as fingerprints, iris texture, earlobe and so on. | |
C1530 | A probability distribution is a list of outcomes and their associated probabilities. A function that represents a discrete probability distribution is called a probability mass function. A function that represents a continuous probability distribution is called a probability density function. | |
C1531 | Independence two jointly continuous random variables X and Y are said to be independent if fX,Y (x,y) = fX(x)fY (y) for all x,y. It is easy to show that X and Y are independent iff any event for X and any event for Y are independent, i.e. for any measurable sets A and B P( X ∈ A ∩ Y ∈ B ) = P(X ∈ A)P(Y ∈ B). | |
C1532 | A randomized algorithm is an algorithm that employs a degree of randomness as part of its logic. In common practice, randomized algorithms are approximated using a pseudorandom number generator in place of a true source of random bits; such an implementation may deviate from the expected theoretical behavior. | |
C1533 | The standard normal (or Z-distribution), is the most common normal distribution, with a mean of 0 and standard deviation of 1. The t-distribution is typically used to study the mean of a population, rather than to study the individuals within a population. | |
C1534 | Knuth Morris Pratt (KMP) is an algorithm, which checks the characters from left to right. When a pattern has a sub-pattern appears more than one in the sub-pattern, it uses that property to improve the time complexity, also for in the worst case. The time complexity of KMP is O(n). | |
C1535 | Hyperparameters are the variables which determines the network structure(Eg: Number of Hidden Units) and the variables which determine how the network is trained(Eg: Learning Rate). Hyperparameters are set before training(before optimizing the weights and bias). | |
C1536 | Bias can enter into algorithmic systems as a result of pre-existing cultural, social, or institutional expectations; because of technical limitations of their design; or by being used in unanticipated contexts or by audiences who are not considered in the software's initial design. | |
C1537 | Weighted regression The idea is to give small weights to observations associated with higher variances to shrink their squared residuals. Weighted regression minimizes the sum of the weighted squared residuals. When you use the correct weights, heteroscedasticity is replaced by homoscedasticity. | |
C1538 | AIC and BIC are Information criteria methods used to assess model fit while penalizing the number of estimated parameters. | |
C1539 | collaborative filtering: user-based for example, CF calculates users' similarities in the item space. matrix factorization amounts to mapping features of user and item via linear combination to latent factor space respectively. | |
C1540 | Maximum pooling, or max pooling, is a pooling operation that calculates the maximum, or largest, value in each patch of each feature map. The results are down sampled or pooled feature maps that highlight the most present feature in the patch, not the average presence of the feature in the case of average pooling. | |
C1541 | The “trick” is that kernel methods represent the data only through a set of pairwise similarity comparisons between the original data observations x (with the original coordinates in the lower dimensional space), instead of explicitly applying the transformations ϕ(x) and representing the data by these transformed | |
C1542 | How to Find the Mean. The mean is the average of the numbers. It is easy to calculate: add up all the numbers, then divide by how many numbers there are. In other words it is the sum divided by the count. | |
C1543 | Artificial Intelligence (AI) is the branch of computer sciences that emphasizes the development of intelligence machines, thinking and working like humans. For example, speech recognition, problem-solving, learning and planning. | |
C1544 | Class limits specify the span of data values that fall within a class. Class boundaries are values halfway between the upper class limit of one class and the lower class limit of the next. Class limits are not possible data values. Class boundaries specify the span of data values that fall within a class. | |
C1545 | Intuitively, the gamma parameter defines how far the influence of a single training example reaches, with low values meaning 'far' and high values meaning 'close'. The gamma parameters can be seen as the inverse of the radius of influence of samples selected by the model as support vectors. | |
C1546 | Implicit or unconscious bias operates outside of the person's awareness and can be in direct contradiction to a person's espoused beliefs and values. What is so dangerous about implicit bias is that it automatically seeps into a person's affect or behavior and is outside of the full awareness of that person. | |
C1547 | Here are 5 common machine learning problems and how you can overcome them.1) Understanding Which Processes Need Automation. 2) Lack of Quality Data. 3) Inadequate Infrastructure. 4) Implementation. 5) Lack of Skilled Resources. | |
C1548 | Automated machine learning benefits This reduces the quality time that they spend in solving critical problems. Automated machine learning changes the making and use of machine learning models with ease and with the predeveloped systems so that the data scientists in the organization can focus more on complex problems. | |
C1549 | A sampling technique where a group of subjects (a sample) for study is selected from a larger group (a population). A non-stratified sample does not take separate samples from strata or sub-groups of a population. | |
C1550 | An input is data that a computer receives. An output is data that a computer sends. Computers only work with digital information. An input device is something you connect to a computer that sends information into the computer. An output device is something you connect to a computer that has information sent to it. | |
C1551 | The Utility of Signal Detection Theory Initially developed by radar researchers in the early 1950s (Peterson et al., 1954), the value of SDT was quickly recognized by cognitive scientists and adapted for application in human decision-making (Tanner and Swets, 1954; Green and Swets, 1966). | |
C1552 | 6 Answers. Machine learning algorithms use optimization all the time. Nonetheless, as mentioned in other answers, convex optimization is faster, simpler and less computationally intensive, so it is often easier to "convexify" a problem (make it convex optimization friendly), then use non-convex optimization. | |
C1553 | Assumptions for the Kruskal Wallis Test One independent variable with two or more levels (independent groups). The test is more commonly used when you have three or more levels. For two levels, consider using the Mann Whitney U Test instead. Ordinal scale, Ratio Scale or Interval scale dependent variables. | |
C1554 | This is the basis of the Breusch–Pagan test. It is a chi-squared test: the test statistic is distributed nχ2 with k degrees of freedom. If the test statistic has a p-value below an appropriate threshold (e.g. p < 0.05) then the null hypothesis of homoskedasticity is rejected and heteroskedasticity assumed. | |
C1555 | The moving average is calculated by adding a stock's prices over a certain period and dividing the sum by the total number of periods. For example, a trader wants to calculate the SMA for stock ABC by looking at the high of day over five periods. | |
C1556 | The adjusted coefficient of determination (also known as adjusted R2 or. pronounced “R bar squared”) is a statistical measure that shows the proportion of variation explained by the estimated regression line. | |
C1557 | In pattern recognition, information retrieval and classification (machine learning), precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances, while recall (also known as sensitivity) is the fraction of the total amount of relevant instances that were | |
C1558 | Preparing Your Dataset for Machine Learning: 8 Basic Techniques That Make Your Data BetterArticulate the problem early.Establish data collection mechanisms.Format data to make it consistent.Reduce data.Complete data cleaning.Decompose data.Rescale data.Discretize data. | |
C1559 | For example, a perfect precision and recall score would result in a perfect F-Measure score:F-Measure = (2 * Precision * Recall) / (Precision + Recall)F-Measure = (2 * 1.0 * 1.0) / (1.0 + 1.0)F-Measure = (2 * 1.0) / 2.0.F-Measure = 1.0. | |
C1560 | z = (x – μ) / σ For example, let's say you have a test score of 190. The test has a mean (μ) of 150 and a standard deviation (σ) of 25. Assuming a normal distribution, your z score would be: z = (x – μ) / σ | |
C1561 | Volume is continuous, so the amount of water would be represented by a continuous random variable. The number of minutes is countable, so it would be a discrete variable. | |
C1562 | A matrix that has only real entries is Hermitian if and only if it is symmetric. A real and symmetric matrix is simply a special case of a Hermitian matrix. | |
C1563 | Maximum likelihood, also called the maximum likelihood method, is the procedure of finding the value of one or more parameters for a given statistic which makes the known likelihood distribution a maximum. The maximum likelihood estimate for a parameter is denoted . For a Bernoulli distribution, (1) | |
C1564 | In short, it ensures each subgroup within the population receives proper representation within the sample. As a result, stratified random sampling provides better coverage of the population since the researchers have control over the subgroups to ensure all of them are represented in the sampling. | |
C1565 | Deep learning (sometimes known as deep structured learning) is a subset of machine learning, where machines employ artificial neural networks to process information. Inspired by biological nodes in the human body, deep learning helps computers to quickly recognize and process images and speech. | |
C1566 | To calculate the standard deviation of those numbers:Work out the Mean (the simple average of the numbers)Then for each number: subtract the Mean and square the result.Then work out the mean of those squared differences.Take the square root of that and we are done! | |
C1567 | Approximate non-negative matrix factorization Usually the number of columns of W and the number of rows of H in NMF are selected so the product WH will become an approximation to V. The full decomposition of V then amounts to the two non-negative matrices W and H as well as a residual U, such that: V = WH + U. | |
C1568 | definition 1: ability to learn quickly. | |
C1569 | 1. a) Higher level of entropy refers to higher state of disorder in the system and it can be reduced by input of energy to lower the entropy. | |
C1570 | The central limit theorem has been extended to the case of dependent random variables by several authors (Bruns, Markoff, S. The conditions under which these theorems are stated either are very restrictive or involve conditional distributions, which makes them difficult to apply. | |
C1571 | Gradient descent is an optimization algorithm used to minimize some function by iteratively moving in the direction of steepest descent as defined by the negative of the gradient. In machine learning, we use gradient descent to update the parameters of our model. | |
C1572 | RPN Loss Function The first term is the classification loss over 2 classes (There is object or not). The second term is the regression loss of bounding boxes only when there is object (i.e. p_i* =1). Thus, RPN network is to pre-check which location contains object. | |
C1573 | Discriminant analysis is a versatile statistical method often used by market researchers to classify observations into two or more groups or categories. In other words, discriminant analysis is used to assign objects to one group among a number of known groups. | |
C1574 | Results/Conclusions In exploratory studies, p-values enable the recognition of any statistically noteworthy findings. Confidence intervals provide information about a range in which the true value lies with a certain degree of probability, as well as about the direction and strength of the demonstrated effect. | |
C1575 | They have too few levels of structure: Neurons, Layers, and Whole Nets. We need to group neurons in each layer in 'capsules' that do a lot of internal computation and then output a compact result.” | |
C1576 | The main difference between DevOps and SRE is that SRE is more operationally driven from the top-down, and it's governed by the developer or development team, instead of the operations team. | |
C1577 | In statistics, the theoretical curve that shows how often an experiment will produce a particular result. The curve is symmetrical and bell shaped, showing that trials will usually give a result near the average, but will occasionally deviate by large amounts. | |
C1578 | The metric system is based upon powers of ten, which is convenient because: A measurement in the metric system that is represented by a rational number remains a rational number after metric unit conversion. (For example, 250 mm = 25 cm = .SI Units.Physical QuantityName of UnitAbbreviationLuminous IntensityLumenIv6 more rows | |
C1579 | A single pixel camera uses only one light sensor to measure the entire image. This allows the use of one really good light sensor as opposed to 10 million very cheap ones. Compressed Sensing is used to measure the entire image using only a single sensor. | |
C1580 | Test-retest reliability is the degree to which test scores remain unchanged when measuring a stable individual characteristic on different occasions. | |
C1581 | 1. A Multi-Agent System (MAS) is a loosely coupled network of software agents that interact to solve problems that are beyond the individual capacities or knowledge of each software agent. Learn more in: Using Multi-Agent Systems to Support e-Health Services. A system composed of multiple interacting intelligent agents | |
C1582 | Causation is the relationship between cause and effect. So, when a cause results in an effect, that's a causation. When we say that correlation does not imply cause, we mean that just because you can see a connection or a mutual relationship between two variables, it doesn't necessarily mean that one causes the other. | |
C1583 | In supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error) is a measure of how accurately an algorithm is able to predict outcome values for previously unseen data. | |
C1584 | In the context of conventional artificial neural networks convergence describes a progression towards a network state where the network has learned to properly respond to a set of training patterns within some margin of error. | |
C1585 | A learning algorithm is a method used to process data to extract patterns appropriate for application in a new situation. In particular, the goal is to adapt a system to a specific input-output transformation task. | |
C1586 | Interaction effects occur when the effect of one variable depends on the value of another variable. In this manner, analysts use models to assess the relationship between each independent variable and the dependent variable. This kind of an effect is called a main effect. | |
C1587 | Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data. It also entails applying data patterns towards effective decision making. | |
C1588 | Elon Musk says he's terrified of AI taking over the world and most scared of Google's DeepMind AI project. Tesla and SpaceX CEO Elon Musk has repeatedly said that he thinks artificial intelligence poses a threat to humanity. | |
C1589 | The receptive field size of a unit can be increased in a number of ways. One option is to stack more layers to make the network deeper, which increases the receptive field size linearly by theory, as each extra layer increases the receptive field size by the kernel size. | |
C1590 | RODGERS APPROACH TO CONCEPT ANALYSIS identify and name the concept of interest; identify the surrogate terms and relevant uses of the concept; select an appropriate realm (sample) for data collection; recognize attributes of the concept;More items | |
C1591 | Unlike the standard boxplot, a modified boxplot does not include the outliers. Instead, the outliers are represented as points beyond the 'whiskers', in order to represent more accurately the dispersion of the data. | |
C1592 | Trainable weights are the weights that will be learnt during the training process. You might see some "strange numbers" because either you are using a pre-trained network that has its weights already learnt or you might be using random initialization when defining the model. | |
C1593 | If the statistical software renders a p value of 0.000 it means that the value is very low, with many "0" before any other digit. So the interpretation would be that the results are significant, same as in the case of other values below the selected threshold for significance. | |
C1594 | Neural networks generally perform supervised learning tasks, building knowledge from data sets where the right answer is provided in advance. The networks then learn by tuning themselves to find the right answer on their own, increasing the accuracy of their predictions. | |
C1595 | The distribution function , also called the cumulative distribution function (CDF) or cumulative frequency function, describes the probability that a variate takes on a value less than or equal to a number . The distribution function is sometimes also denoted. (Evans et al. 2000, p. | |
C1596 | A sampling method is called biased if it systematically favors some outcomes over others. | |
C1597 | Any finite sequence of independent and identically distributed random variables is exchangeable, but the converse is not true. The classic example of a sequence of random variables that's exchangeable but not iid is the sequence of draws you get when sampling without replacement from a finite population. | |
C1598 | Statistical binary classification It is a type of supervised learning, a method of machine learning where the categories are predefined, and is used to categorize new probabilistic observations into said categories. When there are only two categories the problem is known as statistical binary classification. | |
C1599 | three |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.