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C10700 | 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. | |
C10701 | The general idea is that machine learning, while not always the perfect choice, can be powerful in modeling time series data due to its ability to handle non-linear data. The feature engineering applied to the time series data in a machine learning approach is the key to how successful the model will be. | |
C10702 | No, you don't. You'll get an equivalent solution whether you apply some kind of linear scaling or not. Then linear scaling can change the results dramatically. That's actually another reason to do feature scaling, but since you asked about simple linear regression, I won't go into that. | |
C10703 | The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer. | |
C10704 | In basic terms, A MANOVA is an ANOVA with two or more continuous response variables. MANCOVA compares two or more continuous response variables (e.g. Test Scores and Annual Income) by levels of a factor variable (e.g. Level of Education), controlling for a covariate (e.g. Number of Hours Spent Studying). | |
C10705 | The word stochastic is jargon for random. A stochastic process is a system which evolves in time while undergoing chance fluctuations. We can describe such a system by defining a family of random variables, {X t }, where X t measures, at time t, the aspect of the system which is of interest. | |
C10706 | Like random forests, gradient boosting is a set of decision trees. The two main differences are: Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way. | |
C10707 | The problem is we always prefer an output having highest probability or lowest distance from reference as our answer and while we are dealing with it, KNN will always give same output for a given set of input repeatedly tested. That means it is quit deterministic. | |
C10708 | Each class will have a “lower class limit” and an “upper class limit” which are the lowest and highest numbers in each class. The “class width” is the distance between the lower limits of consecutive classes. | |
C10709 | December 1955 | |
C10710 | Definition. The Likelihood Ratio (LR) is the likelihood that a given test result would be expected in a patient with the target disorder compared to the likelihood that that same result would be expected in a patient without the target disorder. | |
C10711 | A chi-square test is used when you want to see if there is a relationship between two categorical variables. In SPSS, the chisq option is used on the statistics subcommand of the crosstabs command to obtain the test statistic and its associated p-value. | |
C10712 | Z-tests are statistical calculations that can be used to compare population means to a sample's. T-tests are calculations used to test a hypothesis, but they are most useful when we need to determine if there is a statistically significant difference between two independent sample groups. | |
C10713 | Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve. | |
C10714 | The notation for the uniform distribution is X ~ U(a, b) where a = the lowest value of x and b = the highest value of x. The probability density function is f(x)=1b−a f ( x ) = 1 b − a for a ≤ x ≤ b. | |
C10715 | Expected Value and Variance. This is also written equivalently as: E(X) = (b + a) / 2. “a” in the formula is the minimum value in the distribution, and “b” is the maximum value. | |
C10716 | When the two options are available, lemmatization will always be a better option than stemming. But if you can apply a lemmatizer, it will always give you a better result, because lemmatizers rely on correct language data (dictionaries) to identify a word with its lemma. | |
C10717 | To summarise, here's what you need to master before being able to learn and understand artificial intelligence:Advanced Math (e.g. correlation algorithms) and Stats.Programming language.Machine Learning.PATIENCE – yes, on top of everything you need lots of patience. | |
C10718 | someone who stands apart from others of his or her group, as by differing behavior, beliefs, or religious practices: scientists who are outliers in their views on climate change. | |
C10719 | A confusion matrix is a table that is often used to describe the performance of a classification model (or "classifier") on a set of test data for which the true values are known. The confusion matrix itself is relatively simple to understand, but the related terminology can be confusing. | |
C10720 | Answer: You would first split the dataset into training and test sets, or perhaps use cross-validation techniques to further segment the dataset into composite sets of training and test sets within the data. | |
C10721 | Examples include path analysis/ regression, repeated measures analysis/latent growth curve modeling, and confirmatory factor analysis. Participants will learn basic skills to analyze data with structural equation modeling. | |
C10722 | ABM is known by different names due to its wide variety of applications, which could refer to entirely diverse methodologies. It can also be called a multi-agent system (MAS) or agent-based system (ABS). | |
C10723 | Unlike Monte Carlo sampling methods that are able to draw independent samples from the distribution, Markov Chain Monte Carlo methods draw samples where the next sample is dependent on the existing sample, called a Markov Chain. | |
C10724 | There are various ways to modify a study design to actively exclude or control confounding variables (3) including Randomization, Restriction and Matching. In randomization the random assignment of study subjects to exposure categories to breaking any links between exposure and confounders. | |
C10725 | 2 Answers. If you have two classes (i.e. binary classification), you should use a binary crossentropy loss. If you have more than two you should use a categorical crossentropy loss. | |
C10726 | Gaussian processes are useful in statistical modelling, benefiting from properties inherited from the normal distribution. For example, if a random process is modelled as a Gaussian process, the distributions of various derived quantities can be obtained explicitly. | |
C10727 | In the Fourier domain image, each point represents a particular frequency contained in the spatial domain image. The Fourier Transform is used in a wide range of applications, such as image analysis, image filtering, image reconstruction and image compression. | |
C10728 | No, you don't have to transform your observed variables just because they don't follow a normal distribution. Linear regression analysis, which includes t-test and ANOVA, does not assume normality for either predictors (IV) or an outcome (DV). No way! Yes, you should check normality of errors AFTER modeling. | |
C10729 | Message passing algorithm which is an iterative decoding algorithm factorizes the global function of many variables into product of simpler local functions, whose arguments are the subset of variables. In order to visualize this factorization we use factor graph. | |
C10730 | Covariance measures the total variation of two random variables from their expected values. Obtain the data.Calculate the mean (average) prices for each asset.For each security, find the difference between each value and mean price.Multiply the results obtained in the previous step.More items | |
C10731 | Histogram normalization is a common technique that is used to enhance fine detail within an image. Each column in the cumulative histogram is computed as the sum of all the image intensity histogram values up to and including that grey level, and then it is scaled so that the final value is 1.0. | |
C10732 | Convergence of random variables (sometimes called stochastic convergence) is where a set of numbers settle on a particular number. In the same way, a sequence of numbers (which could represent cars or anything else) can converge (mathematically, this time) on a single, specific number. | |
C10733 | Test method. Use the one-sample z-test to determine whether the hypothesized population proportion differs significantly from the observed sample proportion. | |
C10734 | In Kalman filtering the "process noise" represents the idea/feature that the state of the system changes over time, but we do not know the exact details of when/how those changes occur, and thus we need to model them as a random process. | |
C10735 | The non-linear functions do the mappings between the inputs and response variables. Their main purpose is to convert an input signal of a node in an ANN(Artificial Neural Network) to an output signal. That output signal is now used as an input in the next layer in the stack. | |
C10736 | Maximum likelihood, also called the maximum likelihood method, is the procedure of finding the value of one or more parameters for a given statistic which makes the known likelihood distribution a maximum. The maximum likelihood estimate for a parameter is denoted . For a Bernoulli distribution, (1) | |
C10737 | Top Machine Learning Algorithms You Should KnowLinear Regression.Logistic Regression.Linear Discriminant Analysis.Classification and Regression Trees.Naive Bayes.K-Nearest Neighbors (KNN)Learning Vector Quantization (LVQ)Support Vector Machines (SVM)More items• | |
C10738 | From a practical standpoint, L1 tends to shrink coefficients to zero whereas L2 tends to shrink coefficients evenly. L1 is therefore useful for feature selection, as we can drop any variables associated with coefficients that go to zero. L2, on the other hand, is useful when you have collinear/codependent features. | |
C10739 | Definition of outliers. An outlier is an observation that lies an abnormal distance from other values in a random sample from a population. In a sense, this definition leaves it up to the analyst (or a consensus process) to decide what will be considered abnormal. | |
C10740 | Descriptive studies only describe the current state of a variable, so there are no presumed cause or effects, therefore no independent and dependent variables. Since neither variable in a correlational design is manipulated, it is impossible to determine which is the cause and which is the effect. | |
C10741 | Abstract. The goal of statistical pattern feature extraction (SPFE) is 'low loss dimension reduction'. As the key link of pattern recognition, dimension reduction has become the research hot spot and difficulty in the fields of pattern recognition, machine learning, data mining and so on. | |
C10742 | Parameter selection: When SVMs are used, there are a number of parameters selected to have the best performance including: (1) parameters included in the kernel functions, (2) the trade-off parameter C, and (3) the ε-insensitivity parameter. | |
C10743 | Solutions to this are to decrease your network size, or to increase dropout. For example you could try dropout of 0.5 and so on. If your training/validation loss are about equal then your model is underfitting. Increase the size of your model (either number of layers or the raw number of neurons per layer) | |
C10744 | As the formula shows, the standard score is simply the score, minus the mean score, divided by the standard deviation. | |
C10745 | Neural network ensemble is a learning paradigm where many neural networks are jointly used to solve a problem. Then it assigns random weights to those networks and employs genetic algorithm to evolve the weights so that they can characterize to some extent the fitness of the neural networks in constituting an ensemble. | |
C10746 | Better Naive Bayes: 12 Tips To Get The Most From The Naive Bayes AlgorithmMissing Data. Naive Bayes can handle missing data. Use Log Probabilities. Use Other Distributions. Use Probabilities For Feature Selection. Segment The Data. Re-compute Probabilities. Use as a Generative Model. Remove Redundant Features.More items• | |
C10747 | The beta function has the formula. B(\alpha,\beta) = \int_{0}^{1} {t^{\alpha-1}(1-t)^{\beta-1}dt} The case where a = 0 and b = 1 is called the standard beta distribution. The equation for the standard beta distribution is. f(x) = \frac{x^{p-1}(1-x)^{q-1}}{B(p,q)} \hspace{.3in} 0 \le x \le 1; p, q > 0. | |
C10748 | A linear model is an equation that describes a relationship between two quantities that show a constant rate of change. | |
C10749 | Disadvantages include its "black box" nature, greater computational burden, proneness to overfitting, and the empirical nature of model development. An overview of the features of neural networks and logistic regression is presented, and the advantages and disadvantages of using this modeling technique are discussed. | |
C10750 | The standard error tells you how accurate the mean of any given sample from that population is likely to be compared to the true population mean. When the standard error increases, i.e. the means are more spread out, it becomes more likely that any given mean is an inaccurate representation of the true population mean. | |
C10751 | Word embeddings are widely used nowadays in Distributional Semantics and for a variety of tasks in NLP. Embeddings can be evaluated using ex- trinsic evaluation methods, i.e. the trained em- beddings are evaluated on a specific task such as part-of-speech tagging or named-entity recogni- tion (Schnabel et al., 2015). | |
C10752 | The advantage of hierarchical clustering is that it is easy to understand and implement. The dendrogram output of the algorithm can be used to understand the big picture as well as the groups in your data. | |
C10753 | The term three-stage least squares (3SLS) refers to a method of estimation that combines system equation, sometimes known as seemingly unrelated regression (SUR), with two-stage least squares estimation. It is assumed that each equation of the system is at least just-identified. | |
C10754 | It measures the overall difference between your data and the values predicted by your estimation model (a “residual” is a measure of the distance from a data point to a regression line). Total SS is related to the total sum and explained sum with the following formula: Total SS = Explained SS + Residual Sum of Squares. | |
C10755 | Lasso regression stands for Least Absolute Shrinkage and Selection Operator. The difference between ridge and lasso regression is that it tends to make coefficients to absolute zero as compared to Ridge which never sets the value of coefficient to absolute zero. | |
C10756 | A learning algorithm is a method used to process data to extract patterns appropriate for application in a new situation. In particular, the goal is to adapt a system to a specific input-output transformation task. | |
C10757 | Experience replay enables reinforcement learning agents to memorize and reuse past experiences, just as humans replay memories for the situation at hand. Contemporary off-policy algorithms either replay past experiences uniformly or utilize a rule- based replay strategy, which may be sub-optimal. | |
C10758 | K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique. K-NN algorithm assumes the similarity between the new case/data and available cases and put the new case into the category that is most similar to the available categories. | |
C10759 | Data Processing is a task of converting data from a given form to a much more usable and desired form i.e. making it more meaningful and informative. Using Machine Learning algorithms, mathematical modelling and statistical knowledge, this entire process can be automated. | |
C10760 | The Chi-square test is intended to test how likely it is that an observed distribution is due to chance. It is also called a "goodness of fit" statistic, because it measures how well the observed distribution of data fits with the distribution that is expected if the variables are independent. | |
C10761 | The most used algorithm to train neural networks is gradient descent. We'll define it later, but for now hold on to the following idea: the gradient is a numeric calculation allowing us to know how to adjust the parameters of a network in such a way that its output deviation is minimized. | |
C10762 | A sequence of random variables is covariance stationary if all the terms of the sequence have the same mean, and if the covariance between any two terms of the sequence depends only on the relative positions of the two terms, that is, on how far apart they are located from each other, and not on their absolute position | |
C10763 | The whole procedure involved is called the sample design. The term sample survey is used for a detailed study of the sample. In general, the term sample survey is used for any study conducted on the sample taken from some real world data. A complete list of all the units in a population is called the sampling frame. | |
C10764 | 4:5317:59Suggested clip · 119 secondsHow to Use SPSS-Hierarchical Multiple Regression - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C10765 | Trends are determined by a combination of volume and how much time it takes to create volume. In other words, one-day growth is trending, while 30 days is just more news. Because the number of tweets using the hashtag, #FreddieGrey, built up over time, volume increased at the same rate of traffic. | |
C10766 | Difference between K Means and Hierarchical clustering Hierarchical clustering can't handle big data well but K Means clustering can. This is because the time complexity of K Means is linear i.e. O(n) while that of hierarchical clustering is quadratic i.e. O(n2). | |
C10767 | Does not affect R2 or adjusted R2 (since these estimate the POPULATION variances which are not conditional on X) | |
C10768 | In linear regression the independent variables can be categorical and/or continuous. But, when you fit the model if you have more than two category in the categorical independent variable make sure you are creating dummy variables. | |
C10769 | 0:382:54Suggested clip · 70 secondsClass Boundaries - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C10770 | A dependent variable is what you measure in the experiment and what is affected during the experiment. The dependent variable responds to the independent variable. It is called dependent because it "depends" on the independent variable. | |
C10771 | To address this issue, there are a few techniques we can apply. One method is to randomly resample from the minority classes (West and East) in our training dataset to meet the highest class-specific sample size, essentially copying random minority records. | |
C10772 | Hidden layers allow for the function of a neural network to be broken down into specific transformations of the data. For example, a hidden layer functions that are used to identify human eyes and ears may be used in conjunction by subsequent layers to identify faces in images. | |
C10773 | Gibbs sampling is commonly used for statistical inference (e.g. determining the best value of a parameter, such as determining the number of people likely to shop at a particular store on a given day, the candidate a voter will most likely vote for, etc.). | |
C10774 | Introduction to Poisson Regression Poisson regression is also a type of GLM model where the random component is specified by the Poisson distribution of the response variable which is a count. When all explanatory variables are discrete, log-linear model is equivalent to poisson regression model. | |
C10775 | The direct approximation of the binomial by the Poisson says that a binomial(n,p) random variable has approximately the same distribution as a Poisson(np) random variable when np is large. | |
C10776 | The first type of process mining is discovery. A discovery technique takes an event log and produces a process model without using any a-priori information. The second type of process mining is conformance. Here, an existing process model is compared with an event log of the same process. | |
C10777 | A rank-2 tensor gets two rotation matrices. This pattern generalizes to tensors of arbitrary rank. In a particular coordinate system, a rank-2 tensor can be expressed as a square matrix, but one should not marry the concepts of tensors and matrices, just like one should think of vectors simply as arrays of numbers. | |
C10778 | A sampling distribution is a probability distribution of a statistic obtained from a larger number of samples drawn from a specific population. It describes a range of possible outcomes that of a statistic, such as the mean or mode of some variable, as it truly exists a population. | |
C10779 | Propel your business processes to the next level with process mining technology and use RPA to increase your organization's productivity. UiPath Process Mining allows businesses to holistically understand their processes and identify process improvement opportunities to increase efficiency and reduce costs. | |
C10780 | There are ways, however, to try to maintain objectivity and avoid bias with qualitative data analysis:Use multiple people to code the data. Have participants review your results. Verify with more data sources. Check for alternative explanations. Review findings with peers. | |
C10781 | The variance of the sampling distribution of the mean is computed as follows: That is, the variance of the sampling distribution of the mean is the population variance divided by N, the sample size (the number of scores used to compute a mean). | |
C10782 | Subsampling reduces the image size by removing information all together. Usually when you subsample, you also interpolate or smooth the image so that you reduce aliasing. Usually, the chrominance values are filtered then subsampled by 1/2 or even 1/4 of that of the intensity. | |
C10783 | Q-learning is called off-policy because the updated policy is different from the behavior policy, so Q-Learning is off-policy. In other words, it estimates the reward for future actions and appends a value to the new state without actually following any greedy policy. | |
C10784 | MAP Growth uses the RIT (Rasch Unit) scale to help you measure and compare academic growth. Specifically, the scale measures levels in academic difficulty. The RIT scale extends equally across all grades, making it possible to compare a student's score at various points throughout his or her education. | |
C10785 | Interpret the key results for Fit Mixed Effects ModelStep 1: Determine whether the random terms significantly affect the response.Step 2: Determine whether the fixed effect terms significantly affect the response.Step 3: Determine how well the model fits your data.Step 4: Evaluate how each level of a fixed effect term affects the response.More items | |
C10786 | The property of maximality of entropy has been used to determine the conditions of equilibrium of an isolated system. | |
C10787 | If you use natural log values for your dependent variable (Y) and keep your independent variables (X) in their original scale, the econometric specification is called a log-linear model. These models are typically used when you think the variables may have an exponential growth relationship. | |
C10788 | The union of two sets is a new set that contains all of the elements that are in at least one of the two sets. The intersection of two sets is a new set that contains all of the elements that are in both sets. | |
C10789 | Fig. 1Determine the number of nearest neighbours (K values).Compute the distance between test sample and all the training samples.Sort the distance and determine nearest neighbours based on the K-th minimum distance.Assemble the categories of the nearest neighbours.More items• | |
C10790 | Local interactions in space can give rise to large scale spatio temporal patterns (e.g. (spiral) waves, spatio-temporal chaos (turbulence), stationary (Turing-type) patterns and transitions between these modes). Their occurrence and properties are largely independent of the precise interaction structure. | |
C10791 | Gradient Descent is an optimization algorithm used for minimizing the cost function in various machine learning algorithms. It is basically used for updating the parameters of the learning model. But if the number of training examples is large, then batch gradient descent is computationally very expensive. | |
C10792 | The term linear model implies that the model is specified as a linear combination of features. Based on training data, the learning process computes one weight for each feature to form a model that can predict or estimate the target value. | |
C10793 | Convergence in distribution means that as n goes to infinity, Xn and Y will have the same distribution function. Convergence in probability means that with probability 1, X = Y. | |
C10794 | Concept Review. In a population whose distribution may be known or unknown, if the size ( n) of samples is sufficiently large, the distribution of the sample means will be approximately normal. The mean of the sample means will equal the population mean. | |
C10795 | The Mutual Information score expresses the extent to which observed frequency of co-occurrence differs from what we would expect (statistically speaking). In statistically pure terms this is a measure of the strength of association between words x and y. | |
C10796 | The Bayes theorem describes the probability of an event based on the prior knowledge of the conditions that might be related to the event. If we know the conditional probability , we can use the bayes rule to find out the reverse probabilities . | |
C10797 | Here is a six-step formula for building your core expert systems.Step One: Define All Deliverables. Step Two: Lay Out the Process. Step Three: Determine the Optimal Level of Expertise for Each Step. Step Four: Control for Consistency. Step Five: Map Out the Key Components of Your Expert System to Refine First.More items• | |
C10798 | Rejection Regions and Alpha Levels You, as a researcher, choose the alpha level you are willing to accept. For example, if you wanted to be 95% confident that your results are significant, you would choose a 5% alpha level (100% – 95%). That 5% level is the rejection region. | |
C10799 | In statistics, a negatively skewed (also known as left-skewed) distribution is a type of distribution in which more values are concentrated on the right side (tail) of the distribution graph while the left tail of the distribution graph is longer. |
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