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C8400 | According to the realistic conflict theory, ingroup bias arises from competition for resources between groups. Since different groups are all competing for the same available resources, it serves the best interests of the group to favor members while spurning outsiders. | |
C8401 | Maximum likelihood estimation involves defining a likelihood function for calculating the conditional probability of observing the data sample given a probability distribution and distribution parameters. This approach can be used to search a space of possible distributions and parameters. | |
C8402 | Independence of Random Variables If two random variables, X and Y, are independent, they satisfy the following conditions. P(x|y) = P(x), for all values of X and Y. P(x ∩ y) = P(x) * P(y), for all values of X and Y. | |
C8403 | 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. After fitting a linear regression model, you need to determine how well the model fits the data. | |
C8404 | 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). | |
C8405 | The logit model uses something called the cumulative distribution function of the logistic distribution. The probit model uses something called the cumulative distribution function of the standard normal distribution to define f(∗). Both functions will take any number and rescale it to fall between 0 and 1. | |
C8406 | 5 Techniques to Prevent Overfitting in Neural NetworksSimplifying The Model. The first step when dealing with overfitting is to decrease the complexity of the model. Early Stopping. Early stopping is a form of regularization while training a model with an iterative method, such as gradient descent. Use Data Augmentation. Use Regularization. Use Dropouts. | |
C8407 | The discount factor essentially determines how much the reinforcement learning agents cares about rewards in the distant future relative to those in the immediate future. If γ=0, the agent will be completely myopic and only learn about actions that produce an immediate reward. | |
C8408 | A Blob is a group of connected pixels in an image that share some common property ( E.g grayscale value ). In the image above, the dark connected regions are blobs, and the goal of blob detection is to identify and mark these regions. | |
C8409 | The purpose of factor analysis is to reduce many individual items into a fewer number of dimensions. Factor analysis can be used to simplify data, such as reducing the number of variables in regression models. | |
C8410 | In statistics and probability, quantiles are cut points dividing the range of a probability distribution into continuous intervals with equal probabilities, or dividing the observations in a sample in the same way. There is one fewer quantile than the number of groups created. | |
C8411 | Our Big Data Hadoop certification training course lets you master the concepts of the Hadoop framework, Big Data tools, and methodologies to prepare you for success in your role as a Big Data Developer. Learn how various components of the Hadoop ecosystem fit into the Big Data processing lifecycle. | |
C8412 | Performing Accuracy Assessment In Erdas Imagine img' that you created in a viewer. Click on the Raster tab –> Classification –> Supervised –> Accuracy Assessment. A new window will open which is the main window for the accuracy assessment tool. A new window will open to set the settings for the accuracy assessment. | |
C8413 | In statistics, maximum likelihood estimation (MLE) is a method of estimating the parameters of a probability distribution by maximizing a likelihood function, so that under the assumed statistical model the observed data is most probable. | |
C8414 | An activation function is a function used in artificial neural networks which outputs a small value for small inputs, and a larger value if its inputs exceed a threshold. Activation functions are useful because they add non-linearities into neural networks, allowing the neural networks to learn powerful operations. | |
C8415 | Spatiotemporal models arise when data are collected across time as well as space and has at least one spatial and one temporal property. An event in a spatiotemporal dataset describes a spatial and temporal phenomenon that exists at a certain time t and location x. | |
C8416 | Categorical Data is the data that generally takes a limited number of possible values. Also, the data in the category need not be numerical, it can be textual in nature. All machine learning models are some kind of mathematical model that need numbers to work with. | |
C8417 | The softmax function is simply a generalisation of the logistic function, which simply squashes values into a given range. the reason for using the softmax is to ensure these logits all sum up to 1, thereby fulfilling the constraints of a probability density. | |
C8418 | Spreading activation is a method for searching associative networks, biological and artificial neural networks, or semantic networks. Spreading activation can also be applied in information retrieval, by means of a network of nodes representing documents and terms contained in those documents. | |
C8419 | Use. Cluster sampling is typically used in market research. It's used when a researcher can't get information about the population as a whole, but they can get information about the clusters. Cluster sampling is often more economical or more practical than stratified sampling or simple random sampling. | |
C8420 | The purpose of the activation function is to introduce non-linearity into the output of a neuron. We know, neural network has neurons that work in correspondence of weight, bias and their respective activation function. | |
C8421 | Introduction[edit] Shift Invariance simply refers to the 'invariance' that a CNN has to recognising images. It allows the CNN to detect features/objects even if it does not look exactly like the images in it's training period. Shift invariance covers 'small' differences, such as movements shifts of a couple of pixels. | |
C8422 | Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. | |
C8423 | How to avoid selection biasesUsing random methods when selecting subgroups from populations.Ensuring that the subgroups selected are equivalent to the population at large in terms of their key characteristics (this method is less of a protection than the first, since typically the key characteristics are not known). | |
C8424 | If your TST (Mantoux) or Quantiferon blood test was found to be positive, this means you have a latent TB infection, but usually not the active disease. | |
C8425 | Since a Naive Bayes text classifier is based on the Bayes's Theorem, which helps us compute the conditional probabilities of occurrence of two events based on the probabilities of occurrence of each individual event, encoding those probabilities is extremely useful. | |
C8426 | Data Structure - Depth First TraversalRule 1 − Visit the adjacent unvisited vertex. Mark it as visited. Display it. Push it in a stack.Rule 2 − If no adjacent vertex is found, pop up a vertex from the stack. (It will pop up all the vertices from the stack, which do not have adjacent vertices.)Rule 3 − Repeat Rule 1 and Rule 2 until the stack is empty. | |
C8427 | An unbiased estimator is an accurate statistic that's used to approximate a population parameter. That's just saying if the estimator (i.e. the sample mean) equals the parameter (i.e. the population mean), then it's an unbiased estimator. | |
C8428 | How to calculate percentileRank the values in the data set in order from smallest to largest.Multiply k (percent) by n (total number of values in the data set). If the index is not a round number, round it up (or down, if it's closer to the lower number) to the nearest whole number.Use your ranked data set to find your percentile. | |
C8429 | In Excel 2016 for Mac: Click Data > Solver. In Excel for Mac 2011: Click the Data tab, under Analysis, click Solver. In Set Objective, enter a cell reference or name for the objective cell. Note: The objective cell must contain a formula. | |
C8430 | . Thus logit regression is simply the GLM when describing it in terms of its link function, and logistic regression describes the GLM in terms of its activation function. | |
C8431 | ANSWER. A false positive means that the results say you have the condition you were tested for, but you really don't. With a false negative, the results say you don't have a condition, but you really do. | |
C8432 | Connection to stratified sampling Quota sampling is the non-probability version of stratified sampling. In stratified sampling, subsets of the population are created so that each subset has a common characteristic, such as gender. | |
C8433 | Repeated measures design is a research design that involves multiple measures of the same variable taken on the same or matched subjects either under different conditions or over two or more time periods. For instance, repeated measurements are collected in a longitudinal study in which change over time is assessed. | |
C8434 | The sampling distribution of the sample mean can be thought of as "For a sample of size n, the sample mean will behave according to this distribution." Any random draw from that sampling distribution would be interpreted as the mean of a sample of n observations from the original population. | |
C8435 | Fine-tuning, in general, means making small adjustments to a process to achieve the desired output or performance. Fine-tuning deep learning involves using weights of a previous deep learning algorithm for programming another similar deep learning process. | |
C8436 | Handling overfittingReduce the network's capacity by removing layers or reducing the number of elements in the hidden layers.Apply regularization , which comes down to adding a cost to the loss function for large weights.Use Dropout layers, which will randomly remove certain features by setting them to zero. | |
C8437 | It is quite simple: if you are running a logit regression, a negative coefficient simply implies that the probability that the event identified by the DV happens decreases as the value of the IV increases. | |
C8438 | Having good test re-test reliability signifies the internal validity of a test and ensures that the measurements obtained in one sitting are both representative and stable over time. | |
C8439 | While Neural Networks use neurons to transmit data in the form of input values and output values through connections, Deep Learning is associated with the transformation and extraction of feature which attempts to establish a relationship between stimuli and associated neural responses present in the brain. | |
C8440 | A target function, in machine learning, is a method for solving a problem that an AI algorithm parses its training data to find. Once an algorithm finds its target function, that function can be used to predict results (predictive analysis). | |
C8441 | If the model fit to the data were correct, the residuals would approximate the random errors that make the relationship between the explanatory variables and the response variable a statistical relationship. Therefore, if the residuals appear to behave randomly, it suggests that the model fits the data well. | |
C8442 | Like random forests, gradient boosting is a set of decision trees. The two main differences are: How trees are built: random forests builds each tree independently while gradient boosting builds one tree at a time. | |
C8443 | : having or involving a number of independent mathematical or statistical variables multivariate calculus multivariate data analysis. | |
C8444 | A linear relationship can also be found in the equation distance = rate x time. Because distance is a positive number (in most cases), this linear relationship would be expressed on the top right quadrant of a graph with an X and Y-axis. | |
C8445 | What a p-value tells you about statistical significance. When you perform a statistical test a p-value helps you determine the significance of your results in relation to the null hypothesis. | |
C8446 | Correspondence Analysis (CA) is a multivariate graphical technique designed to explore relationships among categorical variables. Epidemiologists frequently collect data on multiple categorical variables with to the goal of examining associations amongst these variables. | |
C8447 | Fisher's exact test is a statistical test used to determine if there are nonrandom associations between two categorical variables. . For each one, calculate the associated conditional probability using (2), where the sum of these probabilities must be 1. | |
C8448 | A function of one or more parameters containing a noise term. where the noise is (without loss of generality) assumed to be additive. SEE ALSO: Noise, Stochastic Optimization. | |
C8449 | 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. | |
C8450 | To see what the bias term represents, simply set all to 0. The resulting log odds is the bias term. In other words, the bias term is the "default" log odds for the case that all predictors equal 0 (or equal to reference value for categorical predictors). For example, if = 2.5, then the log odds of the outcome is 2.5. | |
C8451 | The term general linear model (GLM) usually refers to conventional linear regression models for a continuous response variable given continuous and/or categorical predictors. It includes multiple linear regression, as well as ANOVA and ANCOVA (with fixed effects only). | |
C8452 | A distribution in statistics is a function that shows the possible values for a variable and how often they occur. | |
C8453 | 1 Answer. In word2vec, you train to find word vectors and then run similarity queries between words. In doc2vec, you tag your text and you also get tag vectors. If two authors generally use the same words then their vector will be closer. | |
C8454 | Bayesian learning has many advantages over other learning programs: Interpolation Bayesian learning methods interpolate all the way to pure engineering. When faced with any learning problem, there is a choice of how much time and effort a human vs. a computer puts in. | |
C8455 | Linear filters process time-varying input signals to produce output signals, subject to the constraint of linearity. This results from systems composed solely of components (or digital algorithms) classified as having a linear response. | |
C8456 | How to deploy an Object Detection Model with TensorFlow servingCreate a production ready model for TF-Serving. Create TF-serving environment using Docker. Creating a client to request the model server running in the Docker container for inference on a test image. | |
C8457 | In Bayesian statistical inference, a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account. Priors can be created using a number of methods. | |
C8458 | The critical value is a factor used to compute the margin of error, as shown in the equations below. When the sampling distribution of the statistic is normal or nearly normal, the critical value can be expressed as a t score or as a z-score. | |
C8459 | The law of averages is sometimes known as “Gambler's Fallacy. ” It evokes the idea that an event is “due” to happen. The law of averages says it's due to land on black! ” Of course, the wheel has no memory and its probabilities do not change according to past results. | |
C8460 | Restricted Boltzmann Machines are shallow, two-layer neural nets that constitute the building blocks of deep-belief networks. The first layer of the RBM is called the visible, or input layer, and the second is the hidden layer. Each circle represents a neuron-like unit called a node. | |
C8461 | Binomial theorem, statement that for any positive integer n, the nth power of the sum of two numbers a and b may be expressed as the sum of n + 1 terms of the form. Binomial theorem. | |
C8462 | Logistic regression is a classification algorithm, used when the value of the target variable is categorical in nature. Logistic regression is most commonly used when the data in question has binary output, so when it belongs to one class or another, or is either a 0 or 1. | |
C8463 | While implementing the decision tree we will go through the following two phases:Building Phase. Preprocess the dataset. Split the dataset from train and test using Python sklearn package. Train the classifier.Operational Phase. Make predictions. Calculate the accuracy. | |
C8464 | MSE is the average of the squared error that is used as the loss function for least squares regression: It is the sum, over all the data points, of the square of the difference between the predicted and actual target variables, divided by the number of data points. RMSE is the square root of MSE. | |
C8465 | The delta rule is a straight-forward application of gradient descent (i.e. hill climbing), and is easy to do because in a neural network with a single hidden layer, the neurons have direct access to the error signal. | |
C8466 | 5. Image Processing Using Machine LearningFeature mapping using the scale-invariant feature transform (SIFT) algorithm.Image registration using the random sample consensus (RANSAC) algorithm.Image Classification using artificial neural networks.Image classification using convolutional neural networks (CNNs)Image Classification using machine learning.More items | |
C8467 | load_model functionv2. 0. Load a model from a shortcut link, package or data path. If called with a shortcut link or package name, spaCy will assume the model is a Python package and import and call its load() method. | |
C8468 | A term document matrix is a way of representing the words in the text as a table (or matrix) of numbers. The rows of the matrix represent the text responses to be analysed, and the columns of the matrix represent the words from the text that are to be used in the analysis. The most basic version is binary. | |
C8469 | Knowledge-representation is a field of artificial intelligence that focuses on designing computer representations that capture information about the world that can be used to solve complex problems. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system. | |
C8470 | The toss of a coin, throw of a dice and lottery draws are all examples of random events. | |
C8471 | Answer: Agglomerative Hierarchical clustering method allows the clusters to be read from bottom to top and it follows this approach so that the program always reads from the sub-component first then moves to the parent whereas, divisive uses top-bottom approach in which the parent is visited first then the child. | |
C8472 | The slope of a least squares regression can be calculated by m = r(SDy/SDx). In this case (where the line is given) you can find the slope by dividing delta y by delta x. So a score difference of 15 (dy) would be divided by a study time of 1 hour (dx), which gives a slope of 15/1 = 15. | |
C8473 | The name 'exponential smoothing' is attributed to the use of the exponential window function during convolution. It is no longer attributed to Holt, Winters & Brown. , and the weights assigned to previous observations are proportional to the terms of the geometric progression. . | |
C8474 | Face detection and recognition process. The facial recognition process begins with an application for the camera, installed on any compatible device in communication with said camera. This application is then able to use computer vision and a deep neural network in order to find a prospective face within its stream. | |
C8475 | You will need to know the standard deviation of the population in order to calculate the sampling distribution. Add all of the observations together and then divide by the total number of observations in the sample. | |
C8476 | In Bayesian statistical inference, a prior probability distribution, often simply called the prior, of an uncertain quantity is the probability distribution that would express one's beliefs about this quantity before some evidence is taken into account. Priors can be created using a number of methods. | |
C8477 | Galton fit a line to each set of heights, and added a reference line to show the average adult height (68.25 inches). So here's the irony: The term regression, as Galton used it, didn't refer to the statistical procedure he used to determine the fit lines for the plotted data points. | |
C8478 | The terms dummy variable and binary variable are sometimes used interchangeably. However, they are not exactly the same thing. If your dummy variable has only two options, like 1=Male and 2=female, then that dummy variable is also a binary variable. | |
C8479 | In Convolutional Neural Networks, Filters detect spatial patterns such as edges in an image by detecting the changes in intensity values of the image. | |
C8480 | Denying the antecedent, sometimes also called inverse error or fallacy of the inverse, is a formal fallacy of inferring the inverse from the original statement. It is committed by reasoning in the form: If P, then Q. Therefore, if not P, then not Q. | |
C8481 | Summary. A Random Variable is a variable whose possible values are numerical outcomes of a random experiment. Random Variables can be discrete or continuous. An important example of a continuous Random variable is the Standard Normal variable, Z. | |
C8482 | A common strategy is to grow the tree until each node contains a small number of instances then use pruning to remove nodes that do not provide additional information. Pruning should reduce the size of a learning tree without reducing predictive accuracy as measured by a cross-validation set. | |
C8483 | Regularization is a technique which makes slight modifications to the learning algorithm such that the model generalizes better. This in turn improves the model's performance on the unseen data as well. | |
C8484 | Google uses artificial neural networks to power voice search. | |
C8485 | In image processing, thresholding is used to split an image into smaller segments, or junks, using at least one color or gray scale value to define their boundary. The advantage of obtaining first a binary image is that it reduces the complexityof the data and simplifies the process of recognition and classification. | |
C8486 | In statistics and research, internal consistency is typically a measure based on the correlations between different items on the same test (or the same subscale on a larger test). It measures whether several items that propose to measure the same general construct produce similar scores. | |
C8487 | At a high level, there are two families of fairness definitions. "Statistical" definitions of fairness ask for equality of some error metric (like false positive rate) evaluated over "protected" populations. These are easy to check and satisfy, but don't provide guarantees to individuals. | |
C8488 | Quota Sampling also has its pros and cons. As this process sets criteria to choose samples, disadvantages are mainly due to its non-random nature. Some of the disadvantages are as follows: Since quota sampling is a non-random sampling method, it is impossible to find the sampling error. | |
C8489 | To convert a frequency distribution to a probability distribution, divide area of the bar or interval of x by the total area of all the Bars. A simpler formula is: , N is the total Frequency and w is the interval of x. | |
C8490 | Partitioning is when an area of data storage is sub-divided to improve performance. Think of it as an organizational tool. If all your collected data is in one large space without organization the digital tools used for analyzing it will have a more difficult time finding the information in order to analyze it. | |
C8491 | “Statistical significance helps quantify whether a result is likely due to chance or to some factor of interest,” says Redman. When a finding is significant, it simply means you can feel confident that's it real, not that you just got lucky (or unlucky) in choosing the sample. | |
C8492 | Metrics like accuracy, precision, recall are good ways to evaluate classification models for balanced datasets, but if the data is imbalanced and there's class disparity, then other methods like ROC/AUC perform better in evaluating the model performance. | |
C8493 | Introduction to K-Means ClusteringStep 1: Choose the number of clusters k. Step 2: Select k random points from the data as centroids. Step 3: Assign all the points to the closest cluster centroid. Step 4: Recompute the centroids of newly formed clusters. Step 5: Repeat steps 3 and 4. | |
C8494 | This serves the process of symmetry-breaking and gives much better accuracy. In this method, the weights are initialized very close to zero, but randomly. This helps in breaking symmetry and every neuron is no longer performing the same computation. | |
C8495 | The parameters of the distribution are m and s2, where m is the mean (expectation) of the distribution and s2 is the variance. We write X ~ N(m, s2) to mean that the random variable X has a normal distribution with parameters m and s2. If Z ~ N(0, 1), then Z is said to follow a standard normal distribution. | |
C8496 | The Bonferroni procedure ignores dependencies among the data and is therefore much too conservative if the number of tests is large. Hence, we agree with Perneger that the Bonferroni method should not be routinely used. | |
C8497 | In machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular, it is commonly used in support vector machine classification. | |
C8498 | Interpret the key results for Binary Logistic RegressionStep 1: Determine whether the association between the response and the term is statistically significant.Step 2: Understand the effects of the predictors.Step 3: Determine how well the model fits your data.Step 4: Determine whether the model does not fit the data. | |
C8499 | Lastly, the formula for Cohen's Kappa is the probability of agreement take away the probability of random agreement divided by 1 minus the probability of random agreement. Figure 7: Cohen's Kappa coefficient formula. |
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