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C10500 | The name tells you how to calculate it. You subtract the regression-predicted values from the actual values, square them (to get rid of directionality), take their average, then take the square root of the average. | |
C10501 | In statistics, uniform distribution is a probability distribution where all outcomes are equally likely. Discrete uniform distributions have a finite number of outcomes. A continuous uniform distribution is a statistical distribution with an infinite number of equally likely measurable values. | |
C10502 | Most economic variables are constrained to be positive, and their empirical distributions may be quite non-normal (think of the income distribution). When logs are applied, the distributions are better behaved. Taking logs also reduces the extrema in the Page 7 data, and curtails the effects of outliers. | |
C10503 | It is used in studies with a repeated measures or a matched pairs design, where the data meets the requirements for a parametric test (level of measurement is interval or better, data is drawn from a population that has a normal distribution, the variances of the two samples are not significantly different). | |
C10504 | The majority of neural networks are fully connected from one layer to another. These connexions are weighted; the higher the number the greater influence one unit has on another, similar to a human brain. As the data goes through each unit the network is learning more about the data. | |
C10505 | Some of the more common ways to normalize data include:Transforming data using a z-score or t-score. Rescaling data to have values between 0 and 1. Standardizing residuals: Ratios used in regression analysis can force residuals into the shape of a normal distribution.Normalizing Moments using the formula μ/σ.More items | |
C10506 | A score of 1 indicates that the data are one standard deviation from the mean, while a Z-score of -1 places the data one standard deviation below the mean. The higher the Z-score, the further from the norm the data can be considered to be. | |
C10507 | Test Procedure in SPSS StatisticsClick Analyze > Regression > Binary Logistic Transfer the dependent variable, heart_disease, into the Dependent: box, and the independent variables, age, weight, gender and VO2max into the Covariates: box, using the buttons, as shown below: Click on the button.More items | |
C10508 | In many applications including econometrics and biostatistics a fixed effects model refers to a regression model in which the group means are fixed (non-random) as opposed to a random effects model in which the group means are a random sample from a population. | |
C10509 | During the initial stages of survey research, researchers usually prefer using convenience sampling as it's quick and easy to deliver results. Even if many statisticians avoid implementing this technique, it is vital in situations where you intend to get insights in a shorter period or without investing too much money. | |
C10510 | Data wrangling is the process of gathering, selecting, and transforming data to answer an analytical question. Also known as data cleaning or “munging”, legend has it that this wrangling costs analytics professionals as much as 80% of their time, leaving only 20% for exploration and modeling. | |
C10511 | Loss value implies how poorly or well a model behaves after each iteration of optimization. An accuracy metric is used to measure the algorithm's performance in an interpretable way. The accuracy of a model is usually determined after the model parameters and is calculated in the form of a percentage. | |
C10512 | Bayesian statistics are indispensable when what you need is to evaluate a decision or conclusion in light of the available evidence. Quality control would be impossible without Bayesian statistics. | |
C10513 | A normal distribution with a mean of 0 and a standard deviation of 1 is called a standard normal distribution. Since the distribution has a mean of 0 and a standard deviation of 1, the Z column is equal to the number of standard deviations below (or above) the mean. | |
C10514 | - Quora. Non-significant variables on univariate analysis became significant on multivariate analysis? Yes, it is possible that when you add more predictors (X2, X3 and so forth) in a multiple regression, X1 can become a statistically significant predictor. | |
C10515 | ·10 min read. In this article, I will present to you the most sophisticated optimization algorithms in Deep Learning that allow neural networks to learn faster and achieve better performance. These algorithms are Stochastic Gradient Descent with Momentum, AdaGrad, RMSProp, and Adam Optimizer. | |
C10516 | The Binomial Theorem: Formulas. The Binomial Theorem is a quick way (okay, it's a less slow way) of expanding (or multiplying out) a binomial expression that has been raised to some (generally inconveniently large) power. For instance, the expression (3x – 2)10 would be very painful to multiply out by hand. | |
C10517 | And here are seven things you can do about that missing data:Listwise Deletion: Delete all data from any participant with missing values. Recover the Values: You can sometimes contact the participants and ask them to fill out the missing values. | |
C10518 | A frequency distribution is a table that shows “classes” or “intervals” of data entries with a count of the number of entries in each class. The frequency f of a class is the number of data entries in the class. The “class width” is the distance between the lower limits of consecutive classes. | |
C10519 | The goal of training is to minimize a loss. This loss describes the objective that the autoencoder tries to reach. When our goal is to merely reconstruct the input as accurately as possible, two major types of loss function are typically used: Mean squared error and Kullback-Leibler (KL) divergence. | |
C10520 | It's not bad to do, necessarily, but it's not a good habit to get into. Standardising variables when it's not necessary to do so leaves interpretation issues, and can lead to sloppy thinking. Also, remember that standardisation needs to be applied in the same way to all data sets that are used for a given built model. | |
C10521 | Fitting the XGBoost algorithm to conduct a multiclass classification.Data PreperationLoad the RBGlass1 dataset.convert the variable Site from a factor to numeric.Simulate a third class (furnace) from the data.Bind the new class to the original data.Subtract 1 from the Site names so they start at 0.Print out a summary() | |
C10522 | A decision boundary is the region of a problem space in which the output label of a classifier is ambiguous. If the decision surface is a hyperplane, then the classification problem is linear, and the classes are linearly separable. Decision boundaries are not always clear cut. | |
C10523 | So, if you are constrained either by the size of the data or the number of trials you want to try, you may have to go with random forests. There is one fundamental difference in performance between the two that may force you to choose Random Forests over Gradient Boosted Machines (GBMs). | |
C10524 | Linear regression is used to find the best fitting line between all the points of your dataset (by computing the minimum of a given distance), it does not, in itself, reduce the dimensionality of your data. | |
C10525 | 7 Advantages of Robots in the WorkplaceSafety. Safety is the most obvious advantage of utilizing robotics. Speed. Robots don't get distracted or need to take breaks. Consistency. Robots never need to divide their attention between a multitude of things. Perfection. Robots will always deliver quality. Happier Employees. Job Creation. Productivity. | |
C10526 | The criticism was against the claim that Bayes' Theorem should be seen as foundational to the field. The debate went in a philosophical direction, with claims and counterclaims about whether real-world probabilities can ever be known to us. The controversy subsided only when the protagonists retired and died. | |
C10527 | The philosophy of information (PI) is a branch of philosophy that studies topics relevant to computer science, information science and information technology. It includes: the critical investigation of the conceptual nature and basic principles of information, including its dynamics, utilisation and sciences. | |
C10528 | Control Charts: A discrete distribution is one in which the data can only take on certain values, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite). | |
C10529 | Softmax extends this idea into a multi-class world. That is, Softmax assigns decimal probabilities to each class in a multi-class problem. Softmax is implemented through a neural network layer just before the output layer. The Softmax layer must have the same number of nodes as the output layer. | |
C10530 | Since medical tests can't be absolutely true, false positive and false negative are two problems we have to deal with. 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. | |
C10531 | One of the simplest and yet most important models in time series forecasting is the random walk model. This model assumes that in each period the variable takes a random step away from its previous value, and the steps are independently and identically distributed in size (“i.i.d.”). | |
C10532 | Latent Class (LC) segmentation operates under the assumption that there are groups underlying the data that give rise to segments. These groups are "latent" or not directly observable. LC techniques use formal statistical modeling to get at these segments, unlike most other segmentation methods. | |
C10533 | Box and Tiao (1973) define a noninformative prior as a prior which provides little information relative to the experiment. Bernardo and Smith (1994) use a similar definition, they say that noninformative priors have minimal effect relative to the data, on the final inference. | |
C10534 | Answer: An example of a superset can be that if B is a proper superset of A, then all elements of A shall be in B but B shall have at least one element whose existence does not take place in A. In contrast, a proper subset contains elements of the original set but not all. | |
C10535 | Mathematical Statistics TopicsCombinatorics and basic set theory notation.Probability definitions and properties.Common discrete and continuous distributions.Bivariate distributions.Conditional probability.Random variables, expectation, variance.Univariate and bivariate transformations.More items | |
C10536 | In statistics, nonlinear regression is a form of regression analysis in which observational data are modeled by a function which is a nonlinear combination of the model parameters and depends on one or more independent variables. The data are fitted by a method of successive approximations. | |
C10537 | 5:1515:11Suggested clip · 109 secondsStatQuest: Linear Discriminant Analysis (LDA) clearly explained YouTubeStart of suggested clipEnd of suggested clip | |
C10538 | Normality: Data have a normal distribution (or at least is symmetric) Homogeneity of variances: Data from multiple groups have the same variance. Linearity: Data have a linear relationship. Independence: Data are independent. | |
C10539 | The standard score (more commonly referred to as a z-score) is a very useful statistic because it (a) allows us to calculate the probability of a score occurring within our normal distribution and (b) enables us to compare two scores that are from different normal distributions. | |
C10540 | We can reduce the size of a Tensorflow Model using the below mentioned methods: Freezing: Convert the variables stored in a checkpoint file of the SavedModel into constants stored directly in the model graph. This reduces the overall size of the model. | |
C10541 | It is said that because the shape of the constraint in LASSO is a diamond, the least squares solution obtained might touch the corner of the diamond such that it leads to a shrinkage of some variable. However, in ridge regression, because it is a circle, it will often not touch the axis. | |
C10542 | As seen Table 1, for the single label classification, labels (category) are mutually exclusive and each instance is assigned to only one category. On the other hand, in the multi-label classification, the labels are interrelated and each instance corresponds to multiple class labels ( Table 2). | |
C10543 | BFS stands for Breadth First Search. DFS stands for Depth First Search. 2. BFS(Breadth First Search) uses Queue data structure for finding the shortest path. DFS(Depth First Search) uses Stack data structure. | |
C10544 | "Neural plasticity" refers to the capacity of the nervous system to modify itself, functionally and structurally, in response to experience and injury. This chapter discusses how plasticity is necessary not only for neural networks to acquire new functional properties, but also for them to remain robust and stable. | |
C10545 | Hickam's dictum | |
C10546 | Loss Function The localization loss is a smooth L1 loss between the predicted bounding box correction and the true values. The coordinate correction transformation is same as what R-CNN does in bounding box regression. | |
C10547 | Inter-observer variation is the amount of variation between the results obtained by two or more observers examining the same material. Intra-observer variation is the amount of variation one observer experiences when observing the same material more than once. | |
C10548 | In descriptive statistics, a time series is defined as a set of random variables ordered with respect to time. Time series are studied both to interpret a phenomenon, identifying the components of a trend, cyclicity, seasonality and to predict its future values. | |
C10549 | Distance metric learning (or simply, metric learning) aims at automatically constructing task-specific distance metrics from (weakly) supervised data, in a machine learning manner. The learned distance metric can then be used to perform various tasks (e.g., k-NN classification, clustering, information retrieval). | |
C10550 | The Adaptive Sliding Window (ADWIN) algorithm [8] is another popular, window-based detector for coping with concept drift. Assuming a stream of examples x_1,x_2,\ldots , x_n, produced by some distribution at time t, these serve as inputs to ADWIN to produce sliding window W. | |
C10551 | We can use MLE in order to get more robust parameter estimates. Thus, MLE can be defined as a method for estimating population parameters (such as the mean and variance for Normal, rate (lambda) for Poisson, etc.) from sample data such that the probability (likelihood) of obtaining the observed data is maximized. | |
C10552 | In project management terms, an s-curve is a mathematical graph that depicts relevant cumulative data for a project—such as cost or man-hours—plotted against time. An s-curve in project management is typically used to track the progress of a project. | |
C10553 | In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. | |
C10554 | Forward chaining starts from known facts and applies inference rule to extract more data unit it reaches to the goal. Backward chaining starts from the goal and works backward through inference rules to find the required facts that support the goal. Backward chaining reasoning applies a depth-first search strategy. | |
C10555 | Confidence intervals and hypothesis tests are similar in that they are both inferential methods that rely on an approximated sampling distribution. Confidence intervals use data from a sample to estimate a population parameter. Hypothesis tests use data from a sample to test a specified hypothesis. | |
C10556 | In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. An example of a model hyperparameter is the topology and size of a neural network. Examples of algorithm hyperparameters are learning rate and mini-batch size. | |
C10557 | Batch size is a term used in machine learning and refers to the number of training examples utilized in one iteration. Usually, a number that can be divided into the total dataset size. stochastic mode: where the batch size is equal to one. | |
C10558 | Artificial intelligence can dramatically improve the efficiencies of our workplaces and can augment the work humans can do. When AI takes over repetitive or dangerous tasks, it frees up the human workforce to do work they are better equipped for—tasks that involve creativity and empathy among others. | |
C10559 | Gap Statistic Method Hence, the optimal choice of k is the value that maximizes the gap (meaning that the clustering structure is far away from a random uniform distribution of points). We can then use k=5 from the Gap Statistic method to use in KMeans and visualize the clustering result. | |
C10560 | In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] parameterized by two positive shape parameters, denoted by α and β, that appear as exponents of the random variable and control the shape of the distribution. | |
C10561 | A document-term matrix or term-document matrix is a mathematical matrix that describes the frequency of terms that occur in a collection of documents. In a document-term matrix, rows correspond to documents in the collection and columns correspond to terms. | |
C10562 | The reason why Convolutional Neural Networks (CNNs) do so much better than classic neural networks on images and videos is that the convolutional layers take advantage of inherent properties of images. Simple feedforward neural networks don't see any order in their inputs. | |
C10563 | The Wilcoxon signed rank sum test is another example of a non-parametric or distribution free test (see 2.1 The Sign Test). As for the sign test, the Wilcoxon signed rank sum test is used is used to test the null hypothesis that the median of a distribution is equal to some value. | |
C10564 | At every node, a set of possible split points is identified for every predictor variable. The algorithm calculates the improvement in purity of the data that would be created by each split point of each variable. The split with the greatest improvement is chosen to partition the data and create child nodes. | |
C10565 | The sample standard deviation (s) is a point estimate of the population standard deviation (σ). The sample mean (̄x) is a point estimate of the population mean, μ The sample variance (s2 is a point estimate of the population variance (σ2). | |
C10566 | Gradient Boosting Machines vs. XGBoost. While regular gradient boosting uses the loss function of our base model (e.g. decision tree) as a proxy for minimizing the error of the overall model, XGBoost uses the 2nd order derivative as an approximation. | |
C10567 | Hindsight bias is a psychological phenomenon that allows people to convince themselves after an event that they had accurately predicted it before it happened. This can lead people to conclude that they can accurately predict other events. | |
C10568 | An operation which can produce some well-defined outcomes, is called an experiment. Each outcome is called an event. An experiment in which all possible outcomes are known and the exact outcome cannot be predicted in advance, is called a random experiment. | |
C10569 | In statistics, Poisson regression is a generalized linear model form of regression analysis used to model count data and contingency tables. A Poisson regression model is sometimes known as a log-linear model, especially when used to model contingency tables. | |
C10570 | So, assuming a 15% survey response rate, we see that you should send your NPS survey to 1,700 customers. What if you're a smaller company and don't have enough customers to send the recommended number of invitations? | |
C10571 | A random variable can be either discrete (having specific values) or continuous (any value in a continuous range). The use of random variables is most common in probability and statistics, where they are used to quantify outcomes of random occurrences. | |
C10572 | The only difference from Ridge regression is that the regularization term is in absolute value. Lasso method overcomes the disadvantage of Ridge regression by not only punishing high values of the coefficients β but actually setting them to zero if they are not relevant. | |
C10573 | Normal distribution describes continuous data which have a symmetric distribution, with a characteristic 'bell' shape. Binomial distribution describes the distribution of binary data from a finite sample. Poisson distribution describes the distribution of binary data from an infinite sample. | |
C10574 | Bias allows you to shift the activation function by adding a constant (i.e. the given bias) to the input. Bias in Neural Networks can be thought of as analogous to the role of a constant in a linear function, whereby the line is effectively transposed by the constant value. | |
C10575 | Selection sortClassSorting algorithmWorst-case performanceО(n2) comparisons, О(n) swapsBest-case performanceО(n2) comparisons, O(1) swapsAverage performanceО(n2) comparisons, О(n) swapsWorst-case space complexityO(1) auxiliary1 more row | |
C10576 | 6 Types of Regression Models in Machine Learning You Should Know AboutLinear Regression.Logistic Regression.Ridge Regression.Lasso Regression.Polynomial Regression.Bayesian Linear Regression. | |
C10577 | Von Mises stress is a value used to determine if a given material will yield or fracture. It is mostly used for ductile materials, such as metals. | |
C10578 | Bootstrapping is a type of resampling where large numbers of smaller samples of the same size are repeatedly drawn, with replacement, from a single original sample. For example, let's say your sample was made up of ten numbers: 49, 34, 21, 18, 10, 8, 6, 5, 2, 1. You randomly draw three numbers 5, 1, and 49. | |
C10579 | For a large sample size, Sample Variance will be a better estimate of Population variance so even if population variance is unknown, we can use the Z test using sample variance. Similarly, for a Large Sample, we have a high degree of freedom. | |
C10580 | An artificial neural network is an attempt to simulate the network of neurons that make up a human brain so that the computer will be able to learn things and make decisions in a humanlike manner. ANNs are created by programming regular computers to behave as though they are interconnected brain cells. | |
C10581 | A Statistical Model is the use of statistics to build a representation of the data and then conduct analysis to infer any relationships between variables or discover insights. Machine Learning is the use of mathematical and or statistical models to obtain a general understanding of the data to make predictions. | |
C10582 | You probably have a numerical stability issue. This may happen due to zero division or any operation that is making a number(s) extremely big. | |
C10583 | Limitations include its sample size requirements, difficulty of interpretation when there are large numbers of categories (20 or more) in the independent or dependent variables, and tendency of the Cramer's V to produce relative low correlation measures, even for highly significant results. | |
C10584 | There are two types of quantitative data, which is also referred to as numeric data: continuous and discrete. As a general rule, counts are discrete and measurements are continuous. Discrete data is a count that can't be made more precise. Typically it involves integers. | |
C10585 | Typical examples are the linear operator of multiplication by and differentiation in . | |
C10586 | Related units One newton equals one kilogram metre per second squared. Therefore, the unit metre per second squared is equivalent to newton per kilogram, N·kg−1, or N/kg. Thus, the Earth's gravitational field (near ground level) can be quoted as 9.8 metres per second squared, or the equivalent 9.8 N/kg. | |
C10587 | How to Calculate a Confusion MatrixYou need a test dataset or a validation dataset with expected outcome values.Make a prediction for each row in your test dataset.From the expected outcomes and predictions count: The number of correct predictions for each class. | |
C10588 | Like all of the least squares methods discussed so far, weighted least squares is an efficient method that makes good use of small data sets. It also shares the ability to provide different types of easily interpretable statistical intervals for estimation, prediction, calibration and optimization. | |
C10589 | To train a generative model we first collect a large amount of data in some domain (e.g., think millions of images, sentences, or sounds, etc.) and then train a model to generate data like it. The intuition behind this approach follows a famous quote from Richard Feynman: “What I cannot create, I do not understand.” | |
C10590 | Stacked Generalization or “Stacking” for short is an ensemble machine learning algorithm. It involves combining the predictions from multiple machine learning models on the same dataset, like bagging and boosting. | |
C10591 | AUC stands for "Area under the ROC Curve." That is, AUC measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1). Figure 5. AUC (Area under the ROC Curve). AUC provides an aggregate measure of performance across all possible classification thresholds. | |
C10592 | Hash algorithms have been around for decades and are used for applications such as table lookups. For example, you can use a person's name and address as a hash key used by a hash algorithm. The output of the hash algorithm will be a pointer into a table where the person's information will be stored. | |
C10593 | Adam can be looked at as a combination of RMSprop and Stochastic Gradient Descent with momentum. It uses the squared gradients to scale the learning rate like RMSprop and it takes advantage of momentum by using moving average of the gradient instead of gradient itself like SGD with momentum. | |
C10594 | The two ratios are both used in the Capital Assets Pricing Model (CAPM)Alpha= R – Rf – beta (Rm-Rf)R represents the portfolio return.Rf represents the risk-free rate of return.Beta represents the systematic risk of a portfolio.Rm represents the market return, per a benchmark. | |
C10595 | Chi-Square goodness of fit test is a non-parametric test that is used to find out how the observed value of a given phenomena is significantly different from the expected value. In Chi-Square goodness of fit test, sample data is divided into intervals. | |
C10596 | The Sobel filter is used for edge detection. It works by calculating the gradient of image intensity at each pixel within the image. The result shows how abruptly or smoothly the image changes at each pixel, and therefore how likely it is that that pixel represents an edge. | |
C10597 | When you conduct a study that looks at a single variable, that study involves univariate data. For example, you might study a group of college students to find out their average SAT scores or you might study a group of diabetic patients to find their weights. Bivariate data is when you are studying two variables. | |
C10598 | Naive Bayes Classifier example by hand and how to do in Scikit-Learn, You can use any kind of predictor in a naive Bayes classifier, as long as you can specify a discriminative linear models take a mixture of categorical and continuous predictors. | |
C10599 | Train Loss is the value of the objective function that you are minimizing. This value could be a positive or negative number, depending on the specific objective function of your training data. The training loss is calculated over the entire training dataset. |
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