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C800 | A Markov model is a system that produces a Markov chain, and a hidden Markov model is one where the rules for producing the chain are unknown or "hidden." The rules include two probabilities: (i) that there will be a certain observation and (ii) that there will be a certain state transition, given the state of the | |
C801 | Simple logistic regression analysis refers to the regression application with one dichotomous outcome and one independent variable; multiple logistic regression analysis applies when there is a single dichotomous outcome and more than one independent variable. | |
C802 | Moments are are very useful in statistics because they tell you much about your data. There are four commonly used moments in statistics: the mean, variance, skewness, and kurtosis. The mean gives you a measure of center of the data. | |
C803 | Kalman filters are used to optimally estimate the variables of interests when they can't be measured directly, but an indirect measurement is available. They are also used to find the best estimate of states by combining measurements from various sensors in the presence of noise. | |
C804 | The relative efficiency of two procedures is the ratio of their efficiencies, although often this concept is used where the comparison is made between a given procedure and a notional "best possible" procedure. | |
C805 | The emergence of artificial intelligence (AI) and its progressively wider impact on many sectors requires an assessment of its effect on the achievement of the Sustainable Development Goals. Failure to do so could result in gaps in transparency, safety, and ethical standards. | |
C806 | 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. | |
C807 | Type 1 error, in statistical hypothesis testing, is the error caused by rejecting a null hypothesis when it is true. Type II error is the error that occurs when the null hypothesis is accepted when it is not true. Type I error is equivalent to false positive. | |
C808 | Facial recognition is a category of biometric software that maps an individual's facial features mathematically and stores the data as a faceprint. The software uses deep learning algorithms to compare a live capture or digital image to the stored faceprint in order to verify an individual's identity. | |
C809 | Action words, or action verbs, simply express an action. The action is something the subject of the sentence or clause is doing and includes sleeping, sitting, and napping-so even though there is no movement, there is still an action. | |
C810 | Overfitting in Machine Learning Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. This means that the noise or random fluctuations in the training data is picked up and learned as concepts by the model. | |
C811 | How to Choose a Machine Learning Model – Some GuidelinesCollect data.Check for anomalies, missing data and clean the data.Perform statistical analysis and initial visualization.Build models.Check the accuracy.Present the results. | |
C812 | The difference between the hypergeometric and the binomial distributions. For the binomial distribution, the probability is the same for every trial. For the hypergeometric distribution, each trial changes the probability for each subsequent trial because there is no replacement. | |
C813 | Observer bias can be reduced or eliminated by: Screening observers for potential biases. Having clear rules and procedures in place for the experiment. Making sure behaviors are clearly defined. Setting a time frame for: collecting data, for the duration of the experiment, and for experimental parts. | |
C814 | In machine learning, feature learning or representation learning is a set of techniques that allows a system to automatically discover the representations needed for feature detection or classification from raw data. Feature learning can be either supervised or unsupervised. | |
C815 | We can construct a single HMM for all words. Hidden states = all characters in the alphabet. Transition probabilities and initial probabilities are calculated from language model. Observations and observation probabilities are as before. | |
C816 | Ridge regression does not really select variables in the many predictors situation. Both ridge regression and the LASSO can outperform OLS regression in some predictive situations – exploiting the tradeoff between variance and bias in the mean square error. | |
C817 | Intuitively, this selects the parameter values that make the observed data most probable. The specific value that maximizes the likelihood function is called the maximum likelihood estimate. Further, if the function so defined is measurable, then it is called the maximum likelihood estimator. | |
C818 | Cluster analysis, or clustering, is an unsupervised machine learning task. It involves automatically discovering natural grouping in data. Unlike supervised learning (like predictive modeling), clustering algorithms only interpret the input data and find natural groups or clusters in feature space. | |
C819 | Linear regression is one of the most common techniques of regression analysis. Multiple regression is a broader class of regressions that encompasses linear and nonlinear regressions with multiple explanatory variables. | |
C820 | noun. (in an experiment or clinical trial) a group of subjects who are exposed to the variable under study: a lower infection rate in the experimental group that received the vaccine. | |
C821 | A false positive is an outcome where the model incorrectly predicts the positive class. And a false negative is an outcome where the model incorrectly predicts the negative class. In the following sections, we'll look at how to evaluate classification models using metrics derived from these four outcomes. | |
C822 | We see right away that if two matrices have different eigenvalues then they are not similar. Also, if two matrices have the same distinct eigen values then they are similar. Suppose A and B have the same distinct eigenvalues. Then they are both diagonalizable with the same diagonal 2 Page 3 matrix A. | |
C823 | This chapter presents several ways to summarize quantitative data by a typical value (a measure of location, such as the mean, median, or mode) and a measure of how well the typical value represents the list (a measure of spread, such as the range, inter-quartile range, or standard deviation). | |
C824 | Abstract: The k-means algorithm is known to have a time complexity of O(n 2 ), where n is the input data size. This quadratic complexity debars the algorithm from being effectively used in large applications. | |
C825 | From Wikipedia, the free encyclopedia. Quantization is the process of constraining an input from a continuous or otherwise large set of values (such as the real numbers) to a discrete set (such as the integers). | |
C826 | Linear models describe a continuous response variable as a function of one or more predictor variables. They can help you understand and predict the behavior of complex systems or analyze experimental, financial, and biological data. | |
C827 | The k-means clustering algorithm attempts to split a given anonymous data set (a set containing no information as to class identity) into a fixed number (k) of clusters. The resulting classifier is used to classify (using k = 1) the data and thereby produce an initial randomized set of clusters. | |
C828 | There are two main types of decision trees that are based on the target variable, i.e., categorical variable decision trees and continuous variable decision trees.Categorical variable decision tree. Continuous variable decision tree. Assessing prospective growth opportunities.More items | |
C829 | In the large-sample case, a 95% confidence interval estimate for the population mean is given by x̄ ± 1.96σ/ √n. When the population standard deviation, σ, is unknown, the sample standard deviation is used to estimate σ in the confidence interval formula. | |
C830 | Why the Lognormal Distribution is used to Model Stock Prices Since the lognormal distribution is bound by zero on the lower side, it is therefore perfect for modeling asset prices which cannot take negative values. The normal distribution cannot be used for the same purpose because it has a negative side. | |
C831 | Multicollinearity occurs when independent variables in a regression model are correlated. This correlation is a problem because independent variables should be independent. If the degree of correlation between variables is high enough, it can cause problems when you fit the model and interpret the results. | |
C832 | 0.05 | |
C833 | In statistics, importance sampling is a general technique for estimating properties of a particular distribution, while only having samples generated from a different distribution than the distribution of interest. It is related to umbrella sampling in computational physics. | |
C834 | In order to calculate the sample size needed for your survey or experiment, you will need to follow these steps: Determine the total population size.Complete the calculation.Determine the total population size. Decide on a margin of error. Choose a confidence level. Pick a standard of deviation. Complete the calculation. | |
C835 | 6:3017:57Suggested clip · 93 secondsSAS - Logistic Regression - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C836 | R-squared should accurately reflect the percentage of the dependent variable variation that the linear model explains. Your R2 should not be any higher or lower than this value. However, if you analyze a physical process and have very good measurements, you might expect R-squared values over 90%. | |
C837 | Prior probability, in Bayesian statistical inference, is the probability of an event before new data is collected. This is the best rational assessment of the probability of an outcome based on the current knowledge before an experiment is performed. | |
C838 | Big data analytics and data mining are not the same. Both of them involve the use of large data sets, handling the collection of the data or reporting of the data which is mostly used by businesses. However, both big data analytics and data mining are both used for two different operations. | |
C839 | The SVM in particular defines the criterion to be looking for a decision surface that is maximally far away from any data point. This distance from the decision surface to the closest data point determines the margin of the classifier. Figure 15.1 shows the margin and support vectors for a sample problem. | |
C840 | Theory of mind refers to the ability to attribute mental states such as beliefs, desires, goals, and intentions to others, and to understand that these states are different from one's own. A theory of mind makes it possible to understand emotions, infer intentions, and predict behavior. | |
C841 | The F Distribution The distribution of all possible values of the f statistic is called an F distribution, with v1 = n1 - 1 and v2 = n2 - 1 degrees of freedom. The curve of the F distribution depends on the degrees of freedom, v1 and v2. | |
C842 | An example of a false positive is when a particular test designed to detect melanoma, a type of skin cancer , tests positive for the disease, even though the person does not have cancer. | |
C843 | Random forest is a supervised learning algorithm. The "forest" it builds, is an ensemble of decision trees, usually trained with the “bagging” method. The general idea of the bagging method is that a combination of learning models increases the overall result. | |
C844 | 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 | |
C845 | The most frequently used are the Naive Bayes (NB) family of algorithms, Support Vector Machines (SVM), and deep learning algorithms. | |
C846 | The expected value of the sum of several random variables is equal to the sum of their expectations, e.g., E[X+Y] = E[X]+ E[Y] . On the other hand, the expected value of the product of two random variables is not necessarily the product of the expected values. | |
C847 | In ideal conditions, facial recognition systems can have near-perfect accuracy. Verification algorithms used to match subjects to clear reference images (like a passport photo or mugshot) can achieve accuracy scores as high as 99.97% on standard assessments like NIST's Facial Recognition Vendor Test (FRVT). | |
C848 | Intra-rater reliability refers to the consistency a single scorer has with himself when looking at the same data on different occasions. Finally, inter-rater reliability is how often different scorers agree with each other on the same cases. | |
C849 | How to Find a Sample Size Given a Confidence Interval and Width (unknown population standard deviation)za/2: Divide the confidence interval by two, and look that area up in the z-table: .95 / 2 = 0.475. E (margin of error): Divide the given width by 2. 6% / 2. : use the given percentage. 41% = 0.41. : subtract. from 1. | |
C850 | K-means clustering algorithm computes the centroids and iterates until we it finds optimal centroid. In this algorithm, the data points are assigned to a cluster in such a manner that the sum of the squared distance between the data points and centroid would be minimum. | |
C851 | (There are two red fours in a deck of 52, the 4 of hearts and the 4 of diamonds). Conditional probability: p(A|B) is the probability of event A occurring, given that event B occurs. Joint probability is the probability of two events occurring simultaneously. The probability of event A and event B occurring together. | |
C852 | The standard error is a statistical term that measures the accuracy with which a sample distribution represents a population by using standard deviation. In statistics, a sample mean deviates from the actual mean of a population—this deviation is the standard error of the mean. | |
C853 | A Kalman Filter is an algorithm that can predict future positions based on current position. It can also estimate current position better than what the sensor is telling us. It will be used to have better association. | |
C854 | A statistical hypothesis is a formal claim about a state of nature structured within the framework of a statistical model. For example, one could claim that the median time to failure from (acce]erated) electromigration of the chip population described in Section 6.1. | |
C855 | Feature Selection. Feature selection is for filtering irrelevant or redundant features from your dataset. The key difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones. | |
C856 | So, the prediction of stock Prices using machine learning is 100% correct and not 99%. This is theoritically true, and one can prove this mathematically. BUT THE MACHINE LEARNING TECHNIQUES FOR PREDICTION, DOES NOT ABLE TO PREDECT THE PSYCHOLOGICAL FACTORS OF HUMEN , ON THE PRICES OF THE STOCKS and others. | |
C857 | Currently AI is Used is Following Things/Fields:Virtual Assistant or Chatbots.Agriculture and Farming.Autonomous Flying.Retail, Shopping and Fashion.Security and Surveillance.Sports Analytics and Activities.Manufacturing and Production.Live Stock and Inventory Management.More items• | |
C858 | Feature Selection. The key difference between feature selection and extraction is that feature selection keeps a subset of the original features while feature extraction creates brand new ones. | |
C859 | Facebook uses a powerful AI technology to identify people based on their interests, demographics and online activity. | |
C860 | It depends. If the message you want to carry is about the spread and variability of the data, then standard deviation is the metric to use. If you are interested in the precision of the means or in comparing and testing differences between means then standard error is your metric. | |
C861 | A Gaussian filter is a linear filter. It's usually used to blur the image or to reduce noise. If you use two of them and subtract, you can use them for "unsharp masking" (edge detection). The Gaussian filter alone will blur edges and reduce contrast. | |
C862 | A t-test is used to compare the mean of two given samples. Like a z-test, a t-test also assumes a normal distribution of the sample. A t-test is used when the population parameters (mean and standard deviation) are not known. | |
C863 | 1950s | |
C864 | Agents can be grouped into four classes based on their degree of perceived intelligence and capability :Simple Reflex Agents.Model-Based Reflex Agents.Goal-Based Agents.Utility-Based Agents.Learning Agent. | |
C865 | Misleading graphs are sometimes deliberately misleading and sometimes it's just a case of people not understanding the data behind the graph they create. The “classic” types of misleading graphs include cases where: The Vertical scale is too big or too small, or skips numbers, or doesn't start at zero. | |
C866 | The goal of a company should be to achieve the target performance with minimal variation. That will minimize the customer dissatisfaction. A real life example of the Taguchi Loss Function would be the quality of food compared to expiration dates. That is when the orange will taste the best (customer satisfaction). | |
C867 | For example, a two-way ANOVA allows a company to compare worker productivity based on two independent variables, such as salary and skill set. It is utilized to observe the interaction between the two factors and tests the effect of two factors at the same time. | |
C868 | Matrix factorization is a class of collaborative filtering algorithms used in recommender systems. Matrix factorization algorithms work by decomposing the user-item interaction matrix into the product of two lower dimensionality rectangular matrices. | |
C869 | Robust statistics are resistant to outliers. In other words, if your data set contains very high or very low values, then some statistics will be good estimators for population parameters, and some statistics will be poor estimators. | |
C870 | Though SVM is a linear classifier which learns an (n – 1)-dimensional classifier for classification of data into two classes. But SVM it can be used for classifying a non-linear dataset. | |
C871 | A correlation matrix is a table showing correlation coefficients between sets of variables. Each random variable (Xi) in the table is correlated with each of the other values in the table (Xj). The diagonal of the table is always a set of ones, because the correlation between a variable and itself is always 1. | |
C872 | In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector. | |
C873 | XFL teams will have two timeouts per half, one fewer than in the NFL. Halftime is 10 minutes, two minutes less than the NFL. Another attempt to shorten the game is not allowing coaches to challenge an official's ruling. All plays are subject to review by the replay official. | |
C874 | In information theory, the entropy of a random variable is the average level of "information", "surprise", or "uncertainty" inherent in the variable's possible outcomes. The concept of information entropy was introduced by Claude Shannon in his 1948 paper "A Mathematical Theory of Communication". | |
C875 | In artificial neural networks, the activation function of a node defines the output of that node given an input or set of inputs. A standard integrated circuit can be seen as a digital network of activation functions that can be "ON" (1) or "OFF" (0), depending on input. | |
C876 | Population variance (σ2) tells us how data points in a specific population are spread out. It is the average of the distances from each data point in the population to the mean, squared. | |
C877 | The crucial difference between FIR and IIR filter is that the FIR filter provides an impulse response of finite period. As against IIR is a type of filter that generates impulse response of infinite duration for a dynamic system. | |
C878 | A value of zero indicates that there is no relationship between the two variables. Correlation among variables does not (necessarily) imply causation. If the correlation coefficient of two variables is zero, it signifies that there is no linear relationship between the variables. | |
C879 | Anomaly detection (or outlier detection) is the identification of rare items, events or observations which raise suspicions by differing significantly from the majority of the data. | |
C880 | Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. 12 ANOVA is also called the Fisher analysis of variance, and it is the extension of the t- and z-tests. | |
C881 | 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. | |
C882 | Mini-batch training is a combination of batch and stochastic training. Instead of using all training data items to compute gradients (as in batch training) or using a single training item to compute gradients (as in stochastic training), mini-batch training uses a user-specified number of training items. | |
C883 | Poisson Formula. Suppose we conduct a Poisson experiment, in which the average number of successes within a given region is μ. Then, the Poisson probability is: P(x; μ) = (e-μ) (μx) / x! where x is the actual number of successes that result from the experiment, and e is approximately equal to 2.71828. | |
C884 | The simplest solution is to use other activation functions, such as ReLU, which doesn't cause a small derivative. Residual networks are another solution, as they provide residual connections straight to earlier layers. | |
C885 | In the development of the probability function for a discrete random variable, two conditions must be satisfied: (1) f(x) must be nonnegative for each value of the random variable, and (2) the sum of the probabilities for each value of the random variable must equal one. | |
C886 | Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. | |
C887 | GAN Training Step 1 — Select a number of real images from the training set. Step 2 — Generate a number of fake images. This is done by sampling random noise vectors and creating images from them using the generator. Step 3 — Train the discriminator for one or more epochs using both fake and real images. | |
C888 | Markov chains and random walks are examples of random processes i.e. an indexed collection of random variables. Markov chains and random walks are examples of random processes i.e. an indexed collection of random variables. A random walk is a specific kind of random process made up of a sum of iid random variables. | |
C889 | Normalization: Similarly, the goal of normalization is to change the values of numeric columns in the dataset to a common scale, without distorting differences in the ranges of values. So we normalize the data to bring all the variables to the same range. | |
C890 | The odds ratio is the measure of association for a case-control study. It tells us how much higher the odds of exposure is among cases of a disease compared with controls. The odds ratio compares the odds of exposure to the factor of interest among cases to the odds of exposure to the factor among controls. | |
C891 | The law of averages is the commonly held belief that a particular outcome or event will over certain periods of time occur at a frequency that is similar to its probability. Depending on context or application it can be considered a valid common-sense observation or a misunderstanding of probability. | |
C892 | Word2Vec takes texts as training data for a neural network. The resulting embedding captures whether words appear in similar contexts. GloVe focuses on words co-occurrences over the whole corpus. Its embeddings relate to the probabilities that two words appear together. | |
C893 | Active Learning StrategiesGroup Activities. Case-based learning. Case-based learning requires students to apply their knowledge to reach a conclusion about an open-ended, real-world situation. Individual Activities. Application cards. Partner Activities. Role playing. Visual Organizing Activities. Categorizing grids. | |
C894 | KNN algorithm is one of the simplest classification algorithm and it is one of the most used learning algorithms. KNN is a non-parametric, lazy learning algorithm. Its purpose is to use a database in which the data points are separated into several classes to predict the classification of a new sample point. | |
C895 | Three of the more widely used experimental designs are the completely randomized design, the randomized block design, and the factorial design. In a completely randomized experimental design, the treatments are randomly assigned to the experimental units. | |
C896 | Topic modelling can be described as a method for finding a group of words (i.e topic) from a collection of documents that best represents the information in the collection. It can also be thought of as a form of text mining – a way to obtain recurring patterns of words in textual material. | |
C897 | We write the likelihood function as L(\theta;x)=\prod^n_{i=1}f(X_i;\theta) or sometimes just L(θ). Algebraically, the likelihood L(θ ; x) is just the same as the distribution f(x ; θ), but its meaning is quite different because it is regarded as a function of θ rather than a function of x. | |
C898 | Split learning is a new technique developed at the MIT Media Lab's Camera Culture group that allows for participating entities to train machine learning models without sharing any raw data. | |
C899 | Federated Learning is a machine learning setting where the goal is to train a high-quality centralized model with training data distributed over a large number of clients each with unreliable and relatively slow network connections. |
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