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C8700 | Some examples of situations in which standard deviation might help to understand the value of the data:A class of students took a math test. A dog walker wants to determine if the dogs on his route are close in weight or not close in weight. A market researcher is analyzing the results of a recent customer survey.More items | |
C8701 | Two different learning models were introduced that can be used as part of the word2vec approach to learn the word embedding; they are: Continuous Bag-of-Words, or CBOW model. Continuous Skip-Gram Model. | |
C8702 | The smaller the residual standard deviation, the closer is the fit of the estimate to the actual data. In effect, the smaller the residual standard deviation is compared to the sample standard deviation, the more predictive, or useful, the model is. | |
C8703 | By Paul King on Ap in Probability. A random walk refers to any process in which there is no observable pattern or trend; that is, where the movements of an object, or the values taken by a certain variable, are completely random. | |
C8704 | Calculate (1 - the reliability) - that is, subtract the reliability from 1. Take the square root of the amount calculated in step 3. Multiply the amount calculated in step 4 by the standard deviation found in step 1. This is the standard error of measurement. | |
C8705 | In probability theory and related fields, a stochastic or random process is a mathematical object usually defined as a family of random variables. Stochastic processes are widely used as mathematical models of systems and phenomena that appear to vary in a random manner. | |
C8706 | Generalization refers to your model's ability to adapt properly to new, previously unseen data, drawn from the same distribution as the one used to create the model. Estimated Time: 5 minutes Learning Objectives. | |
C8707 | A one-way ANOVA only involves one factor or independent variable, whereas there are two independent variables in a two-way ANOVA. In a one-way ANOVA, the one factor or independent variable analyzed has three or more categorical groups. A two-way ANOVA instead compares multiple groups of two factors. 4. | |
C8708 | A statistic is a characteristic of a sample. Generally, a statistic is used to estimate the value of a population parameter. For instance, suppose we selected a random sample of 100 students from a school with 1000 students. The average height of the sampled students would be an example of a statistic. | |
C8709 | A variance-covariance matrix is a square matrix that contains the variances and covariances associated with several variables. The diagonal elements of the matrix contain the variances of the variables and the off-diagonal elements contain the covariances between all possible pairs of variables. | |
C8710 | In statistics, a unimodal probability distribution or unimodal distribution is a probability distribution which has a single peak. The term "mode" in this context refers to any peak of the distribution, not just to the strict definition of mode which is usual in statistics. | |
C8711 | Common tools for performing an assessment of the internal and external factors impacting on strategic decisions are SWOT, and PEST or PESTEL analysis. | |
C8712 | Covariance provides insight into how two variables are related to one another. More precisely, covariance refers to the measure of how two random variables in a data set will change together. A positive covariance means that the two variables at hand are positively related, and they move in the same direction. | |
C8713 | Estimation, in statistics, any of numerous procedures used to calculate the value of some property of a population from observations of a sample drawn from the population. A point estimate, for example, is the single number most likely to express the value of the property. | |
C8714 | We demonstrated that convolutional neural networks are primarily utilized for text classification tasks while recurrent neural networks are commonly used for natural language generation or machine translation. | |
C8715 | ADVANTAGES OF DIMENSIONAL ANALYSIS : it helps in conversion of one system of units into the other . it is useful in checking the correctness of the given physical relation . it helps in deriving relationship between various physical quantities | |
C8716 | The most common threshold is p < 0.05, which means that the data is likely to occur less than 5% of the time under the null hypothesis. When the p-value falls below the chosen alpha value, then we say the result of the test is statistically significant. | |
C8717 | Parametric alternatives. Another approach to robust estimation of regression models is to replace the normal distribution with a heavy-tailed distribution. A t-distribution with 4–6 degrees of freedom has been reported to be a good choice in various practical situations. | |
C8718 | You can tell if two random variables are independent by looking at their individual probabilities. If those probabilities don't change when the events meet, then those variables are independent. Another way of saying this is that if the two variables are correlated, then they are not independent. | |
C8719 | Adam is a replacement optimization algorithm for stochastic gradient descent for training deep learning models. Adam combines the best properties of the AdaGrad and RMSProp algorithms to provide an optimization algorithm that can handle sparse gradients on noisy problems. | |
C8720 | Expected Population Error Rate (EPER) is the expected rate of error in the population. The rate is usually estimated based on past operating history, previous test results, process observation or walk-through. | |
C8721 | Deep Neural Networks struggle with the vanishing gradient problem because of the way back propagation is done by calculating an error value for each neuron, starting with the output layer working it's way back to the input layer. Back-propagation then uses the chain rule to calculate the gradient for each neuron. | |
C8722 | To calculate the true p-value, we just need to multiply 0.0968 by two, or 0.1936. This would be a p-value of 19.36%. The second method is using a graphing calculator. This can give us a more exact number because we will not have to cut off the z-score at the hundredths place. | |
C8723 | Eigenvalues and eigenvectors allow us to "reduce" a linear operation to separate, simpler, problems. For example, if a stress is applied to a "plastic" solid, the deformation can be dissected into "principle directions"- those directions in which the deformation is greatest. | |
C8724 | A. Disparate Treatment DiscriminationThe employee is a member of a protected class; The discriminator knew of the employee's protected class; Acts of harm occurred; Others who were similarly situated were either treated more favorably or not subjected to the same or similar adverse treatment. | |
C8725 | How to calculate margin of errorGet the population standard deviation (σ) and sample size (n).Take the square root of your sample size and divide it into your population standard deviation.Multiply the result by the z-score consistent with your desired confidence interval according to the following table: | |
C8726 | While a frequency distribution gives the exact frequency or the number of times a data point occurs, a probability distribution gives the probability of occurrence of the given data point. | |
C8727 | noun. the act of turning out; production: the factory's output of cars; artistic output. the quantity or amount produced, as in a given time: to increase one's daily output. the material produced or yield; product. | |
C8728 | 1. The Gaussian Graphical Model. Notably, in the Gaussian graphical model, these lines capture partial correlations, that is, the correlation between two items or variables when controlling for all other items or variables included in the data set. | |
C8729 | The random variable then takes values which are real numbers from the interval [0, 360), with all parts of the range being "equally likely". Any real number has probability zero of being selected, but a positive probability can be assigned to any range of values. | |
C8730 | For omitted variable bias to occur, the omitted variable ”Z” must satisfy two conditions: The omitted variable is correlated with the included regressor (i.e. The omitted variable is a determinant of the dependent variable (i.e. expensive and the alternative funding is loan or scholarship which is harder to acquire. | |
C8731 | Perceptron Learning Rule states that the algorithm would automatically learn the optimal weight coefficients. The input features are then multiplied with these weights to determine if a neuron fires or not. | |
C8732 | The biggest flaw in this machine learning technique, according to Mittu, is that there is a large amount of art to building these networks, which means there are few scientific methods to help understand when they will fail. | |
C8733 | CNNs are used for image classification and recognition because of its high accuracy. The CNN follows a hierarchical model which works on building a network, like a funnel, and finally gives out a fully-connected layer where all the neurons are connected to each other and the output is processed. | |
C8734 | Bayesian inference is a method of statistical inference in which Bayes' theorem is used to update the probability for a hypothesis as more evidence or information becomes available. Bayesian updating is particularly important in the dynamic analysis of a sequence of data. | |
C8735 | The natural logarithm, or logarithm to base e, is the inverse function to the natural exponential function. The natural logarithm of a number k > 1 can be defined directly as the area under the curve y = 1/x between x = 1 and x = k, in which case e is the value of k for which this area equals one (see image). | |
C8736 | PDF according to input X being discrete or continuous generates probability mass functions and CDF does the same but generates cumulative mass function. That means, PDF is derivative of CDF and CDF can be applied at any point where PDF has been applied. The cumulative function is the integral of the density function. | |
C8737 | A machine learning model is a file that has been trained to recognize certain types of patterns. You train a model over a set of data, providing it an algorithm that it can use to reason over and learn from those data. | |
C8738 | Featuretools is a framework to perform automated feature engineering. It excels at transforming temporal and relational datasets into feature matrices for machine learning. | |
C8739 | Systematic vs. Random errors are (like the name suggests) completely random. They are unpredictable and can't be replicated by repeating the experiment again. Systematic Errors produce consistent errors, either a fixed amount (like 1 lb) or a proportion (like 105% of the true value). | |
C8740 | A moving average is a technique that calculates the overall trend in a data set. In operations management, the data set is sales volume from historical data of the company. This technique is very useful for forecasting short-term trends. It is simply the average of a select set of time periods. | |
C8741 | Bayesian inference is a machine learning model not as widely used as deep learning or regression models. | |
C8742 | A linear regression line has an equation of the form Y = a + bX, where X is the explanatory variable and Y is the dependent variable. The slope of the line is b, and a is the intercept (the value of y when x = 0). | |
C8743 | Some applications of unsupervised machine learning techniques include: Clustering allows you to automatically split the dataset into groups according to similarity. Often, however, cluster analysis overestimates the similarity between groups and doesn't treat data points as individuals. | |
C8744 | 4:5510:35Suggested clip · 104 secondsSetting Up a Markov Chain - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C8745 | A t-test tests a null hypothesis about two means; most often, it tests the hypothesis that two means are equal, or that the difference between them is zero. A chi-square test tests a null hypothesis about the relationship between two variables. | |
C8746 | Factor loading is basically the correlation coefficient for the variable and factor. Factor loading shows the variance explained by the variable on that particular factor. In the SEM approach, as a rule of thumb, 0.7 or higher factor loading represents that the factor extracts sufficient variance from that variable. | |
C8747 | The loss is calculated on training and validation and its interpretation is based on how well the model is doing in these two sets. It is the sum of errors made for each example in training or validation sets. Loss value implies how poorly or well a model behaves after each iteration of optimization. | |
C8748 | Summary. Probably approximately correct (PAC) learning is a theoretical framework for analyzing the generalization error of a learning algorithm in terms of its error on a training set and some measure of complexity. The goal is typically to show that an algorithm achieves low generalization error with high probability | |
C8749 | As you have seen, in order to perform a likelihood ratio test, one must estimate both of the models one wishes to compare. The advantage of the Wald and Lagrange multiplier (or score) tests is that they approximate the LR test, but require that only one model be estimated. | |
C8750 | Log-likelihood values cannot be used alone as an index of fit because they are a function of sample size but can be used to compare the fit of different coefficients. Because you want to maximize the log-likelihood, the higher value is better. For example, a log-likelihood value of -3 is better than -7. | |
C8751 | A vector error correction (VEC) model is a restricted VAR designed for use with nonstationary series that are known to be cointegrated. The cointegration term is known as the error correction term since the deviation from long-run equilibrium is corrected gradually through a series of partial short-run adjustments. | |
C8752 | A squashing function is essentially defined as a function that squashes the input to one of the ends of a small interval. In Neural Networks, these can be used at nodes in a hidden layer to squash the input. This introduces non-linearity to the NN and allows the NN to be effective. | |
C8753 | inverse error | |
C8754 | To give you an idea of how drastically CAC can vary, here's a quick look at the average CAC in a variety of industries: Travel: $7. Retail: $10. Consumer Goods: $22. | |
C8755 | Yes, you should check normality of errors AFTER modeling. In linear regression, errors are assumed to follow a normal distribution with a mean of zero. Let's do some simulations and see how normality influences analysis results and see what could be consequences of normality violation. | |
C8756 | Two events are said to be mutually exclusive when the two events cannot occur at the same time. For instance, when you throw a coin the event that a head appears and the event that a tail appears are mutually exclusive because they cannot occur at the same time, it's either a head appears or a tail appears. | |
C8757 | 3:537:13Suggested clip · 71 secondsStatistics With R - 4.4.3C - Bayesian model averaging - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C8758 | The formula for a simple linear regression is:y is the predicted value of the dependent variable (y) for any given value of the independent variable (x).B0 is the intercept, the predicted value of y when the x is 0.B1 is the regression coefficient – how much we expect y to change as x increases.More items• | |
C8759 | In such a sequence of trials, the geometric distribution is useful to model the number of failures before the first success. The distribution gives the probability that there are zero failures before the first success, one failure before the first success, two failures before the first success, and so on. | |
C8760 | An algorithm is considered efficient if its resource consumption, also known as computational cost, is at or below some acceptable level. Roughly speaking, 'acceptable' means: it will run in a reasonable amount of time or space on an available computer, typically as a function of the size of the input. | |
C8761 | Recursive neural network models | |
C8762 | A linear model communication is one-way talking process But the disadvantage is that there is no feedback of the message by the receiver. | |
C8763 | The main reason why we use sigmoid function is because it exists between (0 to 1). Therefore, it is especially used for models where we have to predict the probability as an output. Since probability of anything exists only between the range of 0 and 1, sigmoid is the right choice. The function is differentiable. | |
C8764 | The function fX(x) gives us the probability density at point x. It is the limit of the probability of the interval (x,x+Δ] divided by the length of the interval as the length of the interval goes to 0. Remember that P(x<X≤x+Δ)=FX(x+Δ)−FX(x). =dFX(x)dx=F′X(x),if FX(x) is differentiable at x. | |
C8765 | The attention mechanism is a part of a neural architecture that enables to dynamically highlight relevant features of the input data, which, in NLP, is typically a sequence of textual elements. It can be applied directly to the raw input or to its higher level representation. | |
C8766 | At its core, a loss function is incredibly simple: it's a method of evaluating how well your algorithm models your dataset. If your predictions are totally off, your loss function will output a higher number. If they're pretty good, it'll output a lower number. | |
C8767 | 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. | |
C8768 | If you want to become a better decision-maker, incorporate these nine daily habits into your life.Take Note of Your Overconfidence. Identify the Risks You Take. Frame Your Problems In a Different Way. Stop Thinking About the Problem. Set Aside Time to Reflect on Your Mistakes. Acknowledge Your Shortcuts.More items | |
C8769 | Whereas AI is preprogrammed to carry out a task that a human can but more efficiently, artificial general intelligence (AGI) expects the machine to be just as smart as a human. A machine that was able to do this would be considered a fine example of AGI. | |
C8770 | When one takes half of the difference or variance between the 3rd and the 1st quartiles of a simple distribution or frequency distribution it is quartile deviation. The quartile deviation formula is. Q.D. = Q3-Q1/ 2. Example – Quartiles are values that divide a list of numbers into quarters. | |
C8771 | Ground truth is a term used in statistics and machine learning that means checking the results of machine learning for accuracy against the real world. The term is borrowed from meteorology, where "ground truth" refers to information obtained on site. | |
C8772 | The ReLu (Rectified Linear Unit) Layer ReLu refers to the Rectifier Unit, the most commonly deployed activation function for the outputs of the CNN neurons. Mathematically, it's described as: Unfortunately, the ReLu function is not differentiable at the origin, which makes it hard to use with backpropagation training. | |
C8773 | That is, it entails comparing the observed test statistic to some cutoff value, called the "critical value." If the test statistic is more extreme than the critical value, then the null hypothesis is rejected in favor of the alternative hypothesis. | |
C8774 | The quantile-quantile (q-q) plot is a graphical technique for determining if two data sets come from populations with a common distribution. A q-q plot is a plot of the quantiles of the first data set against the quantiles of the second data set. A 45-degree reference line is also plotted. | |
C8775 | A negative coefficient suggests that as the independent variable increases, the dependent variable tends to decrease. The coefficient value signifies how much the mean of the dependent variable changes given a one-unit shift in the independent variable while holding other variables in the model constant. | |
C8776 | The Taguchi loss function is graphical depiction of loss developed by the Japanese business statistician Genichi Taguchi to describe a phenomenon affecting the value of products produced by a company. This means that if the product dimension goes out of the tolerance limit the quality of the product drops suddenly. | |
C8777 | A baseline is a method that uses heuristics, simple summary statistics, randomness, or machine learning to create predictions for a dataset. You can use these predictions to measure the baseline's performance (e.g., accuracy)-- this metric will then become what you compare any other machine learning algorithm against. | |
C8778 | Example: Finding customer segments Clustering is an unsupervised technique where the goal is to find natural groups or clusters in a feature space and interpret the input data. There are many different clustering algorithms. | |
C8779 | The mean (average) of a data set is found by adding all numbers in the data set and then dividing by the number of values in the set. The median is the middle value when a data set is ordered from least to greatest. The mode is the number that occurs most often in a data set. | |
C8780 | If we know the joint CDF of X and Y, we can find the marginal CDFs, FX(x) and FY(y). Specifically, for any x∈R, we have FXY(x,∞)=P(X≤x,Y≤∞)=P(X≤x)=FX(x). Here, by FXY(x,∞), we mean limy→∞FXY(x,y). Similarly, for any y∈R, we have FY(y)=FXY(∞,y). | |
C8781 | The prior probability of an event will be revised as new data or information becomes available, to produce a more accurate measure of a potential outcome. That revised probability becomes the posterior probability and is calculated using Bayes' theorem. | |
C8782 | Gibbs Sampling is based on sampling from condi- tional distributions of the variables of the posterior. For LDA, we are interested in the latent document-topic portions θd, the topic-word distributions φ(z), and the topic index assignments for each word zi. | |
C8783 | An ROC (Receiver Operating Characteristic) curve is a useful graphical tool to evaluate the performance of a binary classifier as its discrimination threshold is varied. In binary classification, a collection of objects is given, and the task is to classify the objects into two groups based on their features. | |
C8784 | The different types of regression in machine learning techniques are explained below in detail:Linear Regression. Linear regression is one of the most basic types of regression in machine learning. Logistic Regression. Ridge Regression. Lasso Regression. Polynomial Regression. Bayesian Linear Regression. | |
C8785 | A unit of measurement is some specific quantity that has been chosen as the standard against which other measurements of the same kind are made. The term standard refers to the physical object on which the unit of measurement is based. | |
C8786 | AUC (Area under the ROC Curve). AUC provides an aggregate measure of performance across all possible classification thresholds. One way of interpreting AUC is as the probability that the model ranks a random positive example more highly than a random negative example. | |
C8787 | The term random refers to any collection of data or information that has no determined order, or is chosen in a way that is unknown beforehand. For example, 5, 8, 2, 9, and 0 are single-digit numbers listed in random order. Data can be randomly selected, or random numbers can be generated using a random seed. | |
C8788 | Particular distributions are associated with hypothesis testing. Perform tests of a population mean using a normal distribution or a Student's t-distribution. (Remember, use a Student's t-distribution when the population standard deviation is unknown and the distribution of the sample mean is approximately normal.) | |
C8789 | Functions are usually represented by a function rule where you express the dependent variable, y, in terms of the independent variable, x. A pair of an input value and its corresponding output value is called an ordered pair and can be written as (a, b). | |
C8790 | In complete linkage hierarchical clustering, the distance between two clusters is defined as the longest distance between two points in each cluster. For example, the distance between clusters “r” and “s” to the left is equal to the length of the arrow between their two furthest points. | |
C8791 | 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. | |
C8792 | A correlation close to -1 or 1 tells us that there is a strong relationship between the variables. It is useful to know this. Strictly speaking, it applies to a linear relationship, but the correlation can be high even for an obviously curvilinear relationship. | |
C8793 | Forecast bias is distinct from the forecast error and one of the most important keys to improving forecast accuracy. Reducing bias means reducing the forecast input from biased sources. A test case study of how bias was accounted for at the UK Department of Transportation. | |
C8794 | Hypothesis Tests of the Mean and MedianParametric tests (means)Nonparametric tests (medians)1-sample t test1-sample Sign, 1-sample Wilcoxon2-sample t testMann-Whitney testOne-Way ANOVAKruskal-Wallis, Mood's median testFactorial DOE with one factor and one blocking variableFriedman test | |
C8795 | The P value, or calculated probability, is the probability of finding the observed, or more extreme, results when the null hypothesis (H 0) of a study question is true – the definition of 'extreme' depends on how the hypothesis is being tested. | |
C8796 | MedianArrange your numbers in numerical order.Count how many numbers you have.If you have an odd number, divide by 2 and round up to get the position of the median number.If you have an even number, divide by 2. Go to the number in that position and average it with the number in the next higher position to get the median. | |
C8797 | The Dirichlet distribution is a conjugate prior for the multinomial distribution. This means that if the prior distribution of the multinomial parameters is Dirichlet then the posterior distribution is also a Dirichlet distribution (with parameters different from those of the prior). | |
C8798 | Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. | |
C8799 | Rather, the swarm of humans uses software to input their opinions in real time, thus making micro-changes to the rest of the swarm and the inputs of other members. Studies show that swarm intelligence consistently outperforms individuals and crowds working without the algorithms. |
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