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C9600 | Some common types of sampling bias include self-selection, non-response, undercoverage, survivorship, pre-screening or advertising, and healthy user bias. | |
C9601 | The Implicit Association Test (IAT) measures the strength of associations between concepts (e.g., black people, gay people) and evaluations (e.g., good, bad) or stereotypes (e.g., athletic, clumsy). The main idea is that making a response is easier when closely related items share the same response key. | |
C9602 | 1 Answer. The card token is valid for a few minutes (usually up to 10). What Stripe recommends in that case is to use the token now to create a customer via the API first to save its card and then let your background job handle the charge part after the fact. | |
C9603 | Factor Analysis (FA) is an exploratory technique applied to a set of outcome variables that seeks to find the underlying factors (or subsets of variables) from which the observed variables were generated. | |
C9604 | How to read a stock chartIdentify the trend line. This is that blue line you see every time you hear about a stock—it's either going up or down right? Look for lines of support and resistance. Know when dividends and stock splits occur. Understand historic trading volumes. | |
C9605 | Saying that the sample mean is an unbiased estimate of the population mean simply means that there is no systematic distortion that will tend to make it either overestimate or underestimate the population parameter. We run into a problem when we work with the variance, although it is a problem that is easily fixed. | |
C9606 | To calculate the variance follow these steps: Work out the Mean (the simple average of the numbers) Then for each number: subtract the Mean and square the result (the squared difference). Then work out the average of those squared differences. | |
C9607 | The coefficient of variation (CV) is a measure of relative variability. It is the ratio of the standard deviation to the mean (average). For example, the expression “The standard deviation is 15% of the mean” is a CV. | |
C9608 | If we have an irreducible Markov chain, this means that the chain is aperiodic. Since the number 1 is co-prime to every integer, any state with a self-transition is aperiodic. Consider a finite irreducible Markov chain Xn: If there is a self-transition in the chain (pii>0 for some i), then the chain is aperiodic. | |
C9609 | The Loss Function is one of the important components of Neural Networks. Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss Function. In simple words, the Loss is used to calculate the gradients. And gradients are used to update the weights of the Neural Net. | |
C9610 | One way to measure multicollinearity is the variance inflation factor (VIF), which assesses how much the variance of an estimated regression coefficient increases if your predictors are correlated. If no factors are correlated, the VIFs will all be 1. | |
C9611 | 11 Applications of Artificial Intelligence in Business:Chatbots: Artificial Intelligence in eCommerce: AI to Improve Workplace Communication: Human Resource Management: AI in Healthcare: Intelligent Cybersecurity: Artificial Intelligence in Logistics and Supply Chain: Sports betting Industry:More items• | |
C9612 | It repetitively leverages the patterns in residuals, strengthens the model with weak predictions, and make it better. By combining the advantages from both random forest and gradient boosting, XGBoost gave the a prediction error ten times lower than boosting or random forest in my case. | |
C9613 | An example of a nonlinear classifier is kNN. If a problem is nonlinear and its class boundaries cannot be approximated well with linear hyperplanes, then nonlinear classifiers are often more accurate than linear classifiers. If a problem is linear, it is best to use a simpler linear classifier. | |
C9614 | Linear mixed models (sometimes called “multilevel models” or “hierarchical models”, depending on the context) are a type of regression model that take into account both (1) variation that is explained by the independent variables of interest (like lm() ) – fixed effects, and (2) variation that is not explained by the | |
C9615 | Linear regression is called 'Linear regression' not because the x's or the dependent variables are linear with respect to the y or the independent variable but because the parameters or the thetas are. | |
C9616 | Use simple logistic regression when you have one nominal variable and one measurement variable, and you want to know whether variation in the measurement variable causes variation in the nominal variable. | |
C9617 | Predictive modeling is the process of using known results to create, process, and validate a model that can be used to forecast future outcomes. It is a tool used in predictive analytics, a data mining technique that attempts to answer the question "what might possibly happen in the future?" | |
C9618 | Probability limits are used when the parameter is considered as the realization of a random variable with given prior distribution. | |
C9619 | Advertisements. Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of the required value. For this algorithm to work properly, the data collection should be in a sorted form and equally distributed. | |
C9620 | K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970's as a non-parametric technique. | |
C9621 | We use many algorithms such as Naïve Bayes, Decision trees, SVM, Random forest classifier, KNN, and logistic regression for classification. | |
C9622 | The Likelihood-Ratio test (sometimes called the likelihood-ratio chi-squared test) is a hypothesis test that helps you choose the “best” model between two nested models. Model Two has two predictor variables (age,sex). It is “nested” within model one because it has just two of the predictor variables (age, sex). | |
C9623 | Serial dependence refers to the notion that returns evolve nonrandomly; that is, they are correlated with their prior values. One variation of serial dependence is called mean reversion. With mean reversion, returns revert to an average value or asset prices revert to an equilibrium value. | |
C9624 | One of the newest and most effective ways to resolve the vanishing gradient problem is with residual neural networks, or ResNets (not to be confused with recurrent neural networks). ResNets refer to neural networks where skip connections or residual connections are part of the network architecture. | |
C9625 | In probability theory, an experiment or trial (see below) is any procedure that can be infinitely repeated and has a well-defined set of possible outcomes, known as the sample space. An experiment is said to be random if it has more than one possible outcome, and deterministic if it has only one. | |
C9626 | 7:0814:24Suggested clip · 84 secondsBasic Inference in Bayesian Networks - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C9627 | Performance bottlenecks can lead an otherwise functional computer or server to slow down to a crawl. The term “bottleneck” refers to both an overloaded network and the state of a computing device in which one component is unable to keep pace with the rest of the system, thus slowing overall performance. | |
C9628 | n essence, the kappa statistic is a measure of how closely the instances classified by the machine learning classifier matched the data labeled as ground truth, controlling for the accuracy of a random classifier as measured by the expected accuracy. | |
C9629 | Automatic Document Classification Techniques Include:Expectation maximization (EM)Naive Bayes classifier.Instantaneously trained neural networks.Latent semantic indexing.Support vector machines (SVM)Artificial neural network.K-nearest neighbour algorithms.Decision trees such as ID3 or C4.More items• | |
C9630 | The log likelihood This is important because it ensures that the maximum value of the log of the probability occurs at the same point as the original probability function. Therefore we can work with the simpler log-likelihood instead of the original likelihood. | |
C9631 | False confidence in stepwise results The standard errors of the coefficient estimates are underestimated, which makes the confidence intervals too narrow, the t statistics too high, and the p values too low—which leads to overfitting and creates a false confidence in the final model. | |
C9632 | Below are the methods to convert a categorical (string) input to numerical nature:Label Encoder: It is used to transform non-numerical labels to numerical labels (or nominal categorical variables). Convert numeric bins to number: Let's say, bins of a continuous variable are available in the data set (shown below). | |
C9633 | Hierarchical SVMs refer to those methods that decompose the training tasks according to the structure of the taxonomy [4][5][6][10][17][19]. That is, an SVM model is trained to distinguish only among those categories with the same parent node in the taxonomy tree. | |
C9634 | Heterogeneity in statistics means that your populations, samples or results are different. It is the opposite of homogeneity, which means that the population/data/results are the same. A heterogeneous population or sample is one where every member has a different value for the characteristic you're interested in. | |
C9635 | = e−(λ+µ) z! The above computation establishes that the sum of two independent Poisson distributed random variables, with mean values λ and µ, also has Poisson distribution of mean λ + µ. We can easily extend the same derivation to the case of a finite sum of independent Poisson distributed random variables. | |
C9636 | Time Complexity and Space Complexity are two factors which determine which algorithm is better than the other. Time Complexity in a simple way means the amount of time an algorithm takes to run. Space complexity means the amount of space required by the algorithm. | |
C9637 | R-squared is a goodness-of-fit measure for linear regression models. This statistic indicates the percentage of the variance in the dependent variable that the independent variables explain collectively. For instance, small R-squared values are not always a problem, and high R-squared values are not necessarily good! | |
C9638 | For a normal distribution, the average deviation is somewhat less efficient than the standard deviation as a measure of scale, but this advantage quickly reverses for distributions with heavier tails. | |
C9639 | Interpolation is a statistical method by which related known values are used to estimate an unknown price or potential yield of a security. Interpolation is achieved by using other established values that are located in sequence with the unknown value. Interpolation is at root a simple mathematical concept. | |
C9640 | An Artificial Neural Network is an information processing model that is inspired by the way biological nervous systems, such as the brain, process information. They are loosely modeled after the neuronal structure of the mamalian cerebral cortex but on much smaller scales. | |
C9641 | Any LTI filter with output and input can be represented by a difference equation in the form: If at least one of the is not null, the filter is recursive. If the are all zero, it is a non recursive filter usually called FIR (Finite Input Response) filter. This happens both to recursive and non recursive filters. | |
C9642 | Advantages of Offline Training Faculty can easily judge the performance of each student during the class and can work on problem areas. Students who are trained offline usually tend to perform better than online training, if the course content remains the same. One of the reasons is peer's pressure and competition. | |
C9643 | : the ratio of the frequency of a particular event in a statistical experiment to the total frequency. | |
C9644 | Now, for the differences… The Mann-Whitney U is a very simple test that makes almost no assumptions about any underlying distribution. Because the K-S test can assume interval or higher level data, it is a more powerful statistical test than the MW-U, assuming that assumption is valid. | |
C9645 | When you are controlling for a variable x1 in regression, you are trying to determine how the dependent variable (say, y) moves as a function of the other (independent) variables x2, …, xp in the regression model while holding your variable x1 constant. | |
C9646 | Jaccard similarity is good for cases where duplication does not matter, cosine similarity is good for cases where duplication matters while analyzing text similarity. For two product descriptions, it will be better to use Jaccard similarity as repetition of a word does not reduce their similarity. | |
C9647 | Overall, Sentiment analysis may involve the following types of classification algorithms:Linear Regression.Naive Bayes.Support Vector Machines.RNN derivatives LSTM and GRU. | |
C9648 | Artificial intelligence (AI) is wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence. | |
C9649 | Regression is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables). | |
C9650 | Regression analysis is a powerful statistical method that allows you to examine the relationship between two or more variables of interest. While there are many types of regression analysis, at their core they all examine the influence of one or more independent variables on a dependent variable. | |
C9651 | The primary difference between classification and regression decision trees is that, the classification decision trees are built with unordered values with dependent variables. The regression decision trees take ordered values with continuous values. | |
C9652 | “Kernel” is used due to set of mathematical functions used in Support Vector Machine provides the window to manipulate the data. So, Kernel Function generally transforms the training set of data so that a non-linear decision surface is able to transformed to a linear equation in a higher number of dimension spaces. | |
C9653 | Additive error is the error that is added to the true value and does not depend on the true value itself. In other words, the result of the measurement is considered as a sum of the true value and the additive error: where. | |
C9654 | The ith order statistic of a set of n elements is the ith smallest element. For example, the minimum of a set of elements is the first order statistic (i = 1), and the maximum is the nth order statistic (i = n). A median, informally, is the "halfway point" of the set. | |
C9655 | A noncorrelated (simple) subquery obtains its results independently of its containing (outer) statement. A correlated subquery requires values from its outer query in order to execute. | |
C9656 | To recap, Logistic regression is a binary classification method. It can be modelled as a function that can take in any number of inputs and constrain the output to be between 0 and 1. This means, we can think of Logistic Regression as a one-layer neural network. | |
C9657 | Sentiment analysis is extremely useful in social media monitoring as it allows us to gain an overview of the wider public opinion behind certain topics. Social media monitoring tools like Brandwatch Analytics make that process quicker and easier than ever before, thanks to real-time monitoring capabilities. | |
C9658 | What you want is multi-label classification, so you will use Binary Cross-Entropy Loss or Sigmoid Cross-Entropy loss. It is a Sigmoid activation plus a Cross-Entropy loss. | |
C9659 | In statistics, linear regression is a linear approach to modelling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. | |
C9660 | Now we'll check out the proven way to improve the performance(Speed and Accuracy both) of neural network models:Increase hidden Layers. Change Activation function. Change Activation function in Output layer. Increase number of neurons. Weight initialization. More data. Normalizing/Scaling data.More items• | |
C9661 | For example, Q-learning is an off-policy learner. On-policy methods attempt to evaluate or improve the policy that is used to make decisions. In contrast, off-policy methods evaluate or improve a policy different from that used to generate the data.11/04/2020 | |
C9662 | 7 Practical Guidelines for Accurate Statistical Model BuildingRemember that regression coefficients are marginal results. Start with univariate descriptives and graphs. Next, run bivariate descriptives, again including graphs. Think about predictors in sets. Model building and interpreting results go hand-in-hand.More items | |
C9663 | A weather reporter is analyzing the high temperature forecasted for a series of dates versus the actual high temperature recorded on each date. A low standard deviation would show a reliable weather forecast. A class of students took a test in Language Arts. | |
C9664 | It is often pointed out that when ANOVA is applied to just two groups, and when therefore one can calculate both a t-statistic and an F-statistic from the same data, it happens that the two are related by the simple formula: t2 = F. | |
C9665 | Bayes' theorem, named after 18th-century British mathematician Thomas Bayes, is a mathematical formula for determining conditional probability. Conditional probability is the likelihood of an outcome occurring, based on a previous outcome occurring. | |
C9666 | Find all of your absolute errors, xi – x. Add them all up. Divide by the number of errors. For example, if you had 10 measurements, divide by 10.Mean Absolute Errorn = the number of errors,Σ = summation symbol (which means “add them all up”),|xi – x| = the absolute errors. | |
C9667 | Used to test if different populations have the same proportion of individuals with some characteristic. Used to test whether a frequency distribution fits an expected distribution. | |
C9668 | Here is a step-by-step plan to improve your data structure and algorithm skills:Step 1: Understand Depth vs. Step 2: Start the Depth-First Approach—make a list of core questions. Step 3: Master each data structure. Step 4: Spaced Repetition. Step 5: Isolate techniques that are reused. Step 6: Now, it's time for Breadth.More items• | |
C9669 | 2 Answers. By definition the probability density function is the derivative of the distribution function. But distribution function is an increasing function on R thus its derivative is always positive. Assume that probability density of X is -ve in the interval (a, b). | |
C9670 | Each is essentially a component of the prior term. That is, machine learning is a subfield of artificial intelligence. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. | |
C9671 | Accuracy: The error between the real and measured value. Precision: The random spread of measured values around the average measured values. Resolution: The smallest to be distinguished magnitude from the measured value. | |
C9672 | RMS stands for Root Mean Square and TRMS (True RMS) for True Root Mean Square. The TRMS instruments are much more accurate than the RMS when measuring AC current. This is why all the multimeters in PROMAX catalog have True RMS measurement capabilities. | |
C9673 | Class boundaries are the data values which separate classes. They are not part of the classes or the dataset. The lower class boundary of a class is defined as the average of the lower limit of the class in question and the upper limit of the previous class. | |
C9674 | Accuracy: The number of correct predictions made divided by the total number of predictions made. We're going to predict the majority class associated with a particular node as True. i.e. use the larger value attribute from each node. | |
C9675 | Simply stated: the R2 value is simply the square of the correlation coefficient R . The correlation coefficient ( R ) of a model (say with variables x and y ) takes values between −1 and 1 . It describes how x and y are correlated. | |
C9676 | Discriminative learning refers to any classification learning process that classifies by using a model or estimate of the probability P(y\,\vert x) without reference to an explicit estimate of any of P(x), P(y, x), or P(x \vert \,y), where y is a class and x is a description of an object to be classified. | |
C9677 | 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. | |
C9678 | Maximizing the log likelihood is equivalent to minimizing the distance between two distributions, thus is equivalent to minimizing KL divergence, and then the cross entropy. It's not just because optimizers are built to minimize functions, since you can easily minimize -likelihood. | |
C9679 | The Normal Distribution has:mean = median = mode.symmetry about the center.50% of values less than the mean. and 50% greater than the mean. | |
C9680 | For example, medical diagnosis, image processing, prediction, classification, learning association, regression etc. The intelligent systems built on machine learning algorithms have the capability to learn from past experience or historical data. | |
C9681 | First, correlation measures the degree of relationship between two variables. Regression analysis is about how one variable affects another or what changes it triggers in the other. For more on variables and regression, check out our tutorial How to Include Dummy Variables into a Regression. | |
C9682 | The median is usually preferred in these situations because the value of the mean can be distorted by the outliers. However, it will depend on how influential the outliers are. If they do not significantly distort the mean, using the mean as the measure of central tendency will usually be preferred. | |
C9683 | Rule-based machine learning approaches include learning classifier systems, association rule learning, artificial immune systems, and any other method that relies on a set of rules, each covering contextual knowledge. | |
C9684 | Serial correlation causes OLS to no longer be a minimum variance estimator. 3. Serial correlation causes the estimated variances of the regression coefficients to be biased, leading to unreliable hypothesis testing. The t-statistics will actually appear to be more significant than they really are. | |
C9685 | 3.1. Coreference resolution (or anaphora) is an expression, the interpretation of which depends on another word or phrase presented earlier in the text (antecedent). For example, “Tom has a backache. He was injured.” Here the words “Tom” and “He” refer to the same entity. | |
C9686 | The moving-average model specifies that the output variable depends linearly on the current and various past values of a stochastic (imperfectly predictable) term. The moving-average model should not be confused with the moving average, a distinct concept despite some similarities. | |
C9687 | One way that we calculate the predicted probability of such binary events (drop out or not drop out) is using logistic regression. Unlike regular regression, the outcome calculates the predicted probability of mutually exclusive event occuring based on multiple external factors. | |
C9688 | The Linear Regression Equation The equation has the form Y= a + bX, where Y is the dependent variable (that's the variable that goes on the Y axis), X is the independent variable (i.e. it is plotted on the X axis), b is the slope of the line and a is the y-intercept. | |
C9689 | It enables private IP networks that use unregistered IP addresses to connect to the Internet. NAT operates on a router, usually connecting two networks together, and translates the private (not globally unique) addresses in the internal network into legal addresses, before packets are forwarded to another network. | |
C9690 | The mean, or average, IQ is 100. Standard deviations, in most cases, are 15 points. The majority of the population, 68.26%, falls within one standard deviation of the mean (IQ 85-115). | |
C9691 | where Ua is size m × n, Ub is size m × (m - n), and Σa is of size n × n. Then A = UaΣaVH is called the reduced SVD of the matrix A. In this context the SVD defined in Equation (1) is sometimes referred to as the full SVD for contrast. Notice that Ua is not unitary, but it does have orthogonal columns. | |
C9692 | "Bias" in K-Pop is basically someone's most favorite member of an idol group. It is derived from the original way the word is used, to have a bias towards someone. So for example, if someone asks you "Who is your bias?", they're basically asking who your favorite K-Pop idol is of all time. My bias from BTS is Yoongi! | |
C9693 | Chapter 1 introduced the dictionary and the inverted index as the central data structures in information retrieval (IR). The second more subtle advantage of compression is faster transfer of data from disk to memory. | |
C9694 | An example of Multiple stage sampling by clusters – An organization intends to survey to analyze the performance of smartphones across Germany. They can divide the entire country's population into cities (clusters) and select cities with the highest population and also filter those using mobile devices. | |
C9695 | Centroid is generally defined for a two dimensional object and pertains basically to the geometric centre of a body. It is more of shape dependent. Whereas, centre of mass is a point where the entire mass of a body can be assumed to be concentrated. For 2d objects, Centroid and COM will be the same point. | |
C9696 | Principle Component Analysis (PCA) is a common feature extraction method in data science. Technically, PCA finds the eigenvectors of a covariance matrix with the highest eigenvalues and then uses those to project the data into a new subspace of equal or less dimensions. | |
C9697 | Quasi-experiments usually select only a certain range of values of an independent variable, while a typical correlational study measures all available values of an independent variable. | |
C9698 | Classification Error. The classification error Ei of an individual program i depends on the number of samples incorrectly classified (false positives plus false negatives) and is evaluated by the formula: where f is the number of sample cases incorrectly classified, and n is the total number of sample cases. | |
C9699 | All medical tests can be resulted in false positive and false negative errors. A false positive can lead to unnecessary treatment and a false negative can lead to a false diagnostic, which is very serious since a disease has been ignored. |
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