_id stringlengths 2 6 | text stringlengths 3 395 | title stringclasses 1 value |
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C6500 | 1 randomly select k data points to act as centroids.2 calculate cosine similarity between each data point and each centroid. 3 assign each data point to the cluster with which it has the *highest* cosine similarity.4 calculate the average of each cluster to get new centroids.More items | |
C6501 | Events A and B are independent if the equation P(A∩B) = P(A) · P(B) holds true. You can use the equation to check if events are independent; multiply the probabilities of the two events together to see if they equal the probability of them both happening together. | |
C6502 | Loss is often used in the training process to find the "best" parameter values for your model (e.g. weights in neural network). Once you find the optimized parameters above, you use this metrics to evaluate how accurate your model's prediction is compared to the true data. | |
C6503 | Use of efficient procedures and rules by the Inference Engine is essential in deducting a correct, flawless solution. In case of knowledge-based ES, the Inference Engine acquires and manipulates the knowledge from the knowledge base to arrive at a particular solution. | |
C6504 | Conditional probability is the probability of one event occurring with some relationship to one or more other events. For example: Event A is that it is raining outside, and it has a 0.3 (30%) chance of raining today. Event B is that you will need to go outside, and that has a probability of 0.5 (50%). | |
C6505 | rate of change | |
C6506 | There is no widely accepted standard notation for the median, but some authors represent the median of a variable x either as x͂ or as μ1/2 sometimes also M. In any of these cases, the use of these or other symbols for the median needs to be explicitly defined when they are introduced. | |
C6507 | Blocking refers to operations that block further execution until that operation finishes while non-blocking refers to code that doesn't block execution. Or as Node. js docs puts it, blocking is when the execution of additional JavaScript in the Node. js process must wait until a non-JavaScript operation completes. | |
C6508 | Additivity is a property pertaining to a set of interdependent index numbers related by definition or by accounting constraints under which an aggregate is defined as the sum of its components; additivity requires this identity to be preserved when the values of both an aggregate and its components in some reference | |
C6509 | 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. | |
C6510 | One hidden layer is sufficient for the large majority of problems. Usually, each hidden layer contains the same number of neurons. The larger the number of hidden layers in a neural network, the longer it will take for the neural network to produce the output and the more complex problems the neural network can solve. | |
C6511 | In natural language processing, perplexity is a way of evaluating language models. Using the definition of perplexity for a probability model, one might find, for example, that the average sentence xi in the test sample could be coded in 190 bits (i.e., the test sentences had an average log-probability of -190). | |
C6512 | T-tests are about finding differences between two groups on the mean values of some continuous variable. Correlation is about the linear relationship of two (usually continuous) variables. | |
C6513 | This is because a two-tailed test uses both the positive and negative tails of the distribution. In other words, it tests for the possibility of positive or negative differences. A one-tailed test is appropriate if you only want to determine if there is a difference between groups in a specific direction. | |
C6514 | Weinstein | |
C6515 | Factor analysis is an exploratory statistical technique to investigate dimensions and the factor structure underlying a set of variables (items) while cluster analysis is an exploratory statistical technique to group observations (people, things, events) into clusters or groups so that the degree of association is | |
C6516 | Ridge Regression is a technique for analyzing multiple regression data that suffer from multicollinearity. By adding a degree of bias to the regression estimates, ridge regression reduces the standard errors. It is hoped that the net effect will be to give estimates that are more reliable. | |
C6517 | It appears that the median is always closest to the high point (the mode), while the mean tends to be farther out on the tail. In a symmetrical distribution, the mean and the median are both centrally located close to the high point of the distribution. | |
C6518 | Increase the power of your analysis.larger sample size.better data collection (reducing error)better/correct model (more complex model, account for covariates, etc.)use a one-sided test instead of a two-sided test. | |
C6519 | A sampling distribution is a probability distribution of a statistic obtained from a larger number of samples drawn from a specific population. The sampling distribution of a given population is the distribution of frequencies of a range of different outcomes that could possibly occur for a statistic of a population. | |
C6520 | Intelligence has been defined in many ways: the capacity for logic, understanding, self-awareness, learning, emotional knowledge, reasoning, planning, creativity, critical thinking, and problem-solving. Intelligence is most often studied in humans but has also been observed in both non-human animals and in plants. | |
C6521 | Forward propagation is how neural networks make predictions. Input data is “forward propagated” through the network layer by layer to the final layer which outputs a prediction. | |
C6522 | Analysis of Variance (ANOVA) is a statistical method used to test differences between two or more means. It may seem odd that the technique is called "Analysis of Variance" rather than "Analysis of Means." As you will see, the name is appropriate because inferences about means are made by analyzing variance. | |
C6523 | Stratified sampling combines random selection with predetermined weightig of a population's demographic characteristics. Telephone surveys are usually conducted with random phone numbers picked by computer. | |
C6524 | As far as i read in the manual, stream length is simply the number (n) of the sequent number of the random number sample. The bitstream is likely a number of sample size. | |
C6525 | A sampling distribution is a probability distribution of a statistic obtained from a larger number of samples drawn from a specific population. The sampling distribution of a given population is the distribution of frequencies of a range of different outcomes that could possibly occur for a statistic of a population. | |
C6526 | 0:505:06Suggested clip · 106 secondsPredicting with a Neural Network explained - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C6527 | The entropy of a substance can be obtained by measuring the heat required to raise the temperature a given amount, using a reversible process. The standard molar entropy, So, is the entropy of 1 mole of a substance in its standard state, at 1 atm of pressure. | |
C6528 | Linear filtering of an image is accomplished through an operation called convolution. Convolution is a neighborhood operation in which each output pixel is the weighted sum of neighboring input pixels. The matrix of weights is called the convolution kernel, also known as the filter. | |
C6529 | Neural nets are a means of doing machine learning, in which a computer learns to perform some task by analyzing training examples. Most of today's neural nets are organized into layers of nodes, and they're “feed-forward,” meaning that data moves through them in only one direction. | |
C6530 | Master limited partnerships (MLPs) are a business venture that exists in the form of a publicly traded limited partnership. They combine the tax benefits of a private partnership—profits are taxed only when investors receive distributions—with the liquidity of a publicly-traded company (PTP). | |
C6531 | 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. | |
C6532 | Logistic regression is a powerful machine learning algorithm that utilizes a sigmoid function and works best on binary classification problems, although it can be used on multi-class classification problems through the “one vs. all” method. Logistic regression (despite its name) is not fit for regression tasks. | |
C6533 | To find the average, add them together and divide by the number of values (10 in this case). When repeated measurements give different results, we want to know how widely spread the readings are. The spread of values tells us something about the uncertainty of a measurement. | |
C6534 | Logistic regression is a classification algorithm traditionally limited to only two-class classification problems. If you have more than two classes then Linear Discriminant Analysis is the preferred linear classification technique. | |
C6535 | Statistical hypothesis: A statement about the nature of a population. It is often stated in terms of a population parameter. Null hypothesis: A statistical hypothesis that is to be tested. Alternative hypothesis: The alternative to the null hypothesis. | |
C6536 | In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR) and the second for the moving average (MA). | |
C6537 | The family of beta(α,β) distributions is an exponential family. η is called the natural parameter. | |
C6538 | Stepwise regression is a modification of the forward selection so that after each step in which a variable was added, all candidate variables in the model are checked to see if their significance has been reduced below the specified tolerance level. If a nonsignificant variable is found, it is removed from the model. | |
C6539 | Social engineering is a manipulation technique that exploits human error to gain private information, access, or valuables. In cybercrime, these “human hacking” scams tend to lure unsuspecting users into exposing data, spreading malware infections, or giving access to restricted systems. | |
C6540 | On the other hand, when the normal approximation is used to approximate a discrete distribution, a continuity correction can be employed so that we can approximate the probability of a specific value of the discrete distribution. The continuity correction requires adding or subtracting . | |
C6541 | The median is another form of an average. It usually represents the middle number in a given sequence of numbers when it's ordered by rank. | |
C6542 | 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. | |
C6543 | Identifying Confounding A simple, direct way to determine whether a given risk factor caused confounding is to compare the estimated measure of association before and after adjusting for confounding. In other words, compute the measure of association both before and after adjusting for a potential confounding factor. | |
C6544 | Bias is the simplifying assumptions made by the model to make the target function easier to approximate. Variance is the amount that the estimate of the target function will change given different training data. Trade-off is tension between the error introduced by the bias and the variance. | |
C6545 | Summary: The range of a set of data is the difference between the highest and lowest values in the set. To find the range, first order the data from least to greatest. Then subtract the smallest value from the largest value in the set. | |
C6546 | The Altman Z-Score Formula E= Sales / Total Assets (efficiency ratio – measures how much the company's assets are producing in sales). Z-Score Results: Z-Score of < 1.81 represents a company in distress. Z-Score between 1.81 and 2.99 represents the “caution” zone. | |
C6547 | A marginal distribution is a frequency or relative frequency distribution of either the row or column variable in a contingency table. A conditional distribution lists the relative frequency of each category of the response variable, given a specific value of the explanatory variable in a contingency table. | |
C6548 | When used as nouns, quantile means one of the class of values of a variate which divides the members of a batch or sample into equal-sized subgroups of adjacent values or a probability distribution into distributions of equal probability, whereas quartile means any of the three points that divide an ordered | |
C6549 | As the Oxford dictionary states it, Probability means 'The extent to which something is probable; the likelihood of something happening or being the case'. In mathematics too, probability indicates the same – the likelihood of the occurrence of an event. Examples of events can be : Tossing a coin with the head up. | |
C6550 | Use regression analysis to describe the relationships between a set of independent variables and the dependent variable. Regression analysis produces a regression equation where the coefficients represent the relationship between each independent variable and the dependent variable. | |
C6551 | These represent the squares of the deviation from the mean for each measured value of data. Add the squares of errors together. The final step is to find the sum of the values in the third column. The desired result is the SSE, or the sum of squared errors. | |
C6552 | Overfitting in Machine Learning Overfitting refers to a model that models the training data too well. 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. | |
C6553 | 0:3513:46Suggested clip · 70 secondsInterpreting Odds Ratio for Multinomial Logistic Regression using YouTubeStart of suggested clipEnd of suggested clip | |
C6554 | A nonlinear relationship is a type of relationship between two entities in which change in one entity does not correspond with constant change in the other entity. However, nonlinear entities can be related to each other in ways that are fairly predictable, but simply more complex than in a linear relationship. | |
C6555 | The Minimax algorithm helps find the best move, by working backwards from the end of the game. At each step it assumes that player A is trying to maximize the chances of A winning, while on the next turn player B is trying to minimize the chances of A winning (i.e., to maximize B's own chances of winning). | |
C6556 | You CAN use linear regression with ordinal data, because you can regress any set of numbers against any other. | |
C6557 | Gaussian process regression (GPR) is a nonparametric, Bayesian approach to regression that is making waves in the area of machine learning. GPR has several benefits, working well on small datasets and having the ability to provide uncertainty measurements on the predictions. | |
C6558 | Hierarchical clustering, also known as hierarchical cluster analysis, is an algorithm that groups similar objects into groups called clusters. The endpoint is a set of clusters, where each cluster is distinct from each other cluster, and the objects within each cluster are broadly similar to each other. | |
C6559 | The uncertainty of the difference between two means is greater than the uncertainty in either mean. So the SE of the difference is greater than either SEM, but is less than their sum. | |
C6560 | Linear Activation Function A linear activation function takes the form: A = cx. It takes the inputs, multiplied by the weights for each neuron, and creates an output signal proportional to the input. In one sense, a linear function is better than a step function because it allows multiple outputs, not just yes and no. | |
C6561 | Auxiliary Classifiers are type of architectural component that seek to improve the convergence of very deep networks. They are classifier heads we attach to layers before the end of the network. | |
C6562 | Here is a brief review of our original seven techniques for dimensionality reduction:Missing Values Ratio. Low Variance Filter. High Correlation Filter. Random Forests/Ensemble Trees. Principal Component Analysis (PCA). Backward Feature Elimination. Forward Feature Construction. | |
C6563 | Step 1: Calculate the rate of selection for each group. (Divide by the number of persons selected from a group by the number available from that group.) Step 2: Determine which group has the lowest selection rate, other than 0%. | |
C6564 | The obvious difference between ANOVA and a "Multivariate Analysis of Variance" (MANOVA) is the “M”, which stands for multivariate. In basic terms, A MANOVA is an ANOVA with two or more continuous response variables. Like ANOVA, MANOVA has both a one-way flavor and a two-way flavor. | |
C6565 | The mean average precision (mAP) or sometimes simply just referred to as AP is a popular metric used to measure the performance of models doing document/information retrival and object detection tasks. | |
C6566 | An RBF is a function that changes with distance from a location. For example, suppose the radial basis function is simply the distance from each location, so it forms an inverted cone over each location. If you take a cross section of the x,z plane for y = 5, you will see a slice of each radial basis function. | |
C6567 | A Z-score is a score which indicates how many standard deviations an observation is from the mean of the distribution. Z-scores tend to be used mainly in the context of the normal curve, and their interpretation based on the standard normal table. Non-normal distributions can also be transformed into sets of Z-scores. | |
C6568 | Gaussian process regression (GPR) is a nonparametric, Bayesian approach to regression that is making waves in the area of machine learning. GPR has several benefits, working well on small datasets and having the ability to provide uncertainty measurements on the predictions. | |
C6569 | 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. | |
C6570 | Selection bias can result when the selection of subjects into a study or their likelihood of being retained in the study leads to a result that is different from what you would have gotten if you had enrolled the entire target population. | |
C6571 | The Central limit Theorem states that when sample size tends to infinity, the sample mean will be normally distributed. The Law of Large Number states that when sample size tends to infinity, the sample mean equals to population mean. | |
C6572 | 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. | |
C6573 | Metrics for Evaluating Machine Learning Algorithms Different performance metrics are used to evaluate different Machine Learning Algorithms. For example a classifier used to distinguish between images of different objects; we can use classification performance metrics such as, Log-Loss, Average Accuracy, AUC, etc. | |
C6574 | K-Means clustering algorithm instead converses on local minima which might also correspond to the global minima in some cases but not always. But that is done by simply making the algorithm choose the set of same random no. for each run. | |
C6575 | The t-distribution, also known as Student's t-distribution, is a way of describing data that follow a bell curve when plotted on a graph, with the greatest number of observations close to the mean and fewer observations in the tails. | |
C6576 | Confusion matrix not only gives you insight into the errors being made by your classifier but also types of errors that are being made. This breakdown helps you to overcomes the limitation of using classification accuracy alone. Every column of the confusion matrix represents the instances of that predicted class. | |
C6577 | mAP (mean Average Precision) for Object DetectionPrecision & recall.Precision measures how accurate is your predictions. Recall measures how good you find all the positives. IoU (Intersection over union)Precision is the proportion of TP = 2/3 = 0.67.Recall is the proportion of TP out of the possible positives = 2/5 = 0.4. | |
C6578 | IBM SPSS Statistics for Mac is the ultimate tool for managing your statistics data and research. This super-app affords you complete control over your data. | |
C6579 | Let A and G be the Arithmetic Means and Geometric Means respectively of two positive numbers a and b. Then, As, a and b are positive numbers, it is obvious that A > G when G = -√ab. This proves that the Arithmetic Mean of two positive numbers can never be less than their Geometric Means. | |
C6580 | The principle of maximum likelihood is a method of obtaining the optimum values of the parameters that define a model. And while doing so, you increase the likelihood of your model reaching the “true” model. | |
C6581 | In a paired sample t-test, the observations are defined as the differences between two sets of values, and each assumption refers to these differences, not the original data values. The paired sample t-test has four main assumptions: The observations are independent of one another. | |
C6582 | Analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences among group means and their associated procedures (such as "variation" among and between groups). ANOVAs are useful for comparing (testing) three or more means (groups or variables) for statistical significance. | |
C6583 | The LSTM does have the ability to remove or add information to the cell state, carefully regulated by structures called gates. Gates are a way to optionally let information through. They are composed out of a sigmoid neural net layer and a pointwise multiplication operation. | |
C6584 | Data visualization is an important skill in applied statistics and machine learning. Statistics does indeed focus on quantitative descriptions and estimations of data. Data visualization provides an important suite of tools for gaining a qualitative understanding. | |
C6585 | 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. | |
C6586 | Let's discuss some advantages and disadvantages of Linear Regression. Logistic regression is easier to implement, interpret, and very efficient to train. If the number of observations is lesser than the number of features, Logistic Regression should not be used, otherwise, it may lead to overfitting. | |
C6587 | In this article, we'll cover some of the frameworks set around deep learning and neural networks, including:TensorFlow.Keras.PyTorch.Theano.DL4J.Caffe.Chainer.Microsoft CNTK. | |
C6588 | A deep Boltzmann machine (DBM) is a type of binary pairwise Markov random field (undirected probabilistic graphical model) with multiple layers of hidden random variables. It is a network of symmetrically coupled stochastic binary units. It comprises a set of visible units and layers of hidden units . | |
C6589 | Eigenvalues represent the total amount of variance that can be explained by a given principal component. They can be positive or negative in theory, but in practice they explain variance which is always positive. If eigenvalues are greater than zero, then it's a good sign. | |
C6590 | normal approximation: The process of using the normal curve to estimate the shape of the distribution of a data set. central limit theorem: The theorem that states: If the sum of independent identically distributed random variables has a finite variance, then it will be (approximately) normally distributed. | |
C6591 | Regularized regression is a type of regression where the coefficient estimates are constrained to zero. The magnitude (size) of coefficients, as well as the magnitude of the error term, are penalized. “Regularization” is a way to give a penalty to certain models (usually overly complex ones). | |
C6592 | The difference between standard deviation and standard error is based on the difference between the description of data and its inference.Comparison Chart.Basis for ComparisonStandard DeviationStandard ErrorFormulaSquare root of varianceStandard deviation divided by square root of sample size.5 more rows• | |
C6593 | It's already automating manual and repetitive tasks. Soon it will augment human decisions. Along the way, it will add more to global GDP by 2030 than the current output of China and India—combined. That growth will be more than enough to create many good jobs, while it will also change how current jobs are being done. | |
C6594 | There is no rule for determining what size of correlation is considered strong, moderate or weak. For this kind of data, we generally consider correlations above 0.4 to be relatively strong; correlations between 0.2 and 0.4 are moderate, and those below 0.2 are considered weak. | |
C6595 | A single layer perceptron (SLP) is a feed-forward network based on a threshold transfer function. SLP is the simplest type of artificial neural networks and can only classify linearly separable cases with a binary target (1 , 0). | |
C6596 | Machine learning is a method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that systems can learn from data, identify patterns and make decisions with minimal human intervention. Importance. | |
C6597 | The kernel size here refers to the widthxheight of the filter mask. The max pooling layer, for example, returns the pixel with maximum value from a set of pixels within a mask (kernel). That kernel is swept across the input, subsampling it. | |
C6598 | Communalities – This is the proportion of each variable's variance that can be explained by the factors (e.g., the underlying latent continua). It is also noted as h2 and can be defined as the sum of squared factor loadings for the variables. | |
C6599 | Both LSA and LDA have same input which is Bag of words in matrix format. LSA focus on reducing matrix dimension while LDA solves topic modeling problems. |
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