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C6900 | The standard error (SE) of a statistic (usually an estimate of a parameter) is the standard deviation of its sampling distribution or an estimate of that standard deviation. Mathematically, the variance of the sampling distribution obtained is equal to the variance of the population divided by the sample size. | |
C6901 | Natural language processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of natural language data. | |
C6902 | Regression analysis is used when you want to predict a continuous dependent variable from a number of independent variables. If the dependent variable is dichotomous, then logistic regression should be used. | |
C6903 | Coef. A regression coefficient describes the size and direction of the relationship between a predictor and the response variable. Coefficients are the numbers by which the values of the term are multiplied in a regression equation. | |
C6904 | A term-document matrix represents the processed text from a text analysis as a table or matrix where the rows represent the text responses, or documents, and the columns represent the words or phrases (the terms). matrix). | |
C6905 | We can use the regression line to predict values of Y given values of X. For any given value of X, we go straight up to the line, and then move horizontally to the left to find the value of Y. The predicted value of Y is called the predicted value of Y, and is denoted Y'. | |
C6906 | This occurs when the line-of-best-fit for describing the relationship between x and y is a straight line. The linear relationship between two variables is positive when both increase together; in other words, as values of x get larger values of y get larger. This is also known as a direct relationship. | |
C6907 | One way to show the performance of a reinforcement learning algorithm is to plot the cumulative reward (the sum of all rewards received so far) as a function of the number of steps. One algorithm dominates another if its plot is consistently above the other. | |
C6908 | Under the efficient-frontier framework, the assumption that investors are risk-averse, i.e., they prefer returns and distaste risks. In other words, investors prefer higher returns and lower risks. The dominance principle is usually used to illustrate the risk-return trade-off. | |
C6909 | It is acknowledged that current tests do not measure IQ to a level of accuracy of one point: there is a margin of error, usually considered to be about five points either side of the obtained IQ, which should be taken into account when making a diagnosis of ID (The American Association on Mental Retardation 2002). | |
C6910 | How to approach analysing a datasetstep 1: divide data into response and explanatory variables. The first step is to categorise the data you are working with into “response” and “explanatory” variables. step 2: define your explanatory variables. step 3: distinguish whether response variables are continuous. step 4: express your hypotheses. | |
C6911 | import tensorflow as tf. import datetime. model = create_model() model. %tensorboard --logdir logs/fit. A brief overview of the dashboards shown (tabs in top navigation bar): train_dataset = tf. data. loss_object = tf. keras. # Define our metrics. current_time = datetime. %tensorboard --logdir logs/gradient_tape.More items | |
C6912 | Cluster cohesion: Measures the closeness of the objects within the same cluster. A “lower within-cluster” variation indicates good compactness or good clustering. The separation method is implied to measure how well a cluster is separated from other clusters. | |
C6913 | Moments are a set of statistical parameters to measure a distribution. Four moments are commonly used: 1st, Mean: the average. 2d, Variance: Standard deviation is the square root of the variance: an indication of how closely the values are spread about the mean. | |
C6914 | t-test is used to test if two sample have the same mean. The assumptions are that they are samples from normal distribution. f-test is used to test if two sample have the same variance. Same assumptions hold. | |
C6915 | A partition of a set X is a set of non-empty subsets of X such that every element x in X is in exactly one of these subsets (i.e., X is a disjoint union of the subsets). | |
C6916 | A vector space is a space of vectors, ie. each element is a vector. A vector field is, at its core, a function between some space and some vector space, so every point in our base space has a vector assigned to it. A good example would be wind direction maps you see on weather reports. | |
C6917 | Qualitative Differences The population standard deviation is a parameter, which is a fixed value calculated from every individual in the population. A sample standard deviation is a statistic. This means that it is calculated from only some of the individuals in a population. | |
C6918 | If there are more variables than equations, you cannot find a unique solution, because there isnt one. | |
C6919 | Partitioning methods: Given a set of n objects, a partitioning method constructs k partitions of the data, where each partition represents a cluster and k ≤ n. That is, it divides the data into k groups such that each group must contain at least one object. | |
C6920 | Collaborative filtering (CF) is a technique used by recommender systems. In the newer, narrower sense, collaborative filtering is a method of making automatic predictions (filtering) about the interests of a user by collecting preferences or taste information from many users (collaborating). | |
C6921 | Logits are the raw scores output by the last layer of a neural network. Before activation takes place. | |
C6922 | A weak correlation means that as one variable increases or decreases, there is a lower likelihood of there being a relationship with the second variable. Earthquake magnitude and the depth at which it was measured is therefore weakly correlated, as you can see the scatter plot is nearly flat. | |
C6923 | Usually, statistical significance is determined by calculating the probability of error (p value) by the t ratio. The difference between two groups (such as an experiment vs. control group) is judged to be statistically significant when p = 0.05 or less. | |
C6924 | Univariate is a term commonly used in statistics to describe a type of data which consists of observations on only a single characteristic or attribute. A simple example of univariate data would be the salaries of workers in industry. | |
C6925 | Stride is a parameter of the neural network's filter that modifies the amount of movement over the image or video. For example, if a neural network's stride is set to 1, the filter will move one pixel, or unit, at a time. | |
C6926 | Bias can damage research, if the researcher chooses to allow his bias to distort the measurements and observations or their interpretation. When faculty are biased about individual students in their courses, they may grade some students more or less favorably than others, which is not fair to any of the students. | |
C6927 | With the LassoCV, RidgeCV, and Linear Regression machine learning algorithms.Define the problem.Gather the data.Clean & Explore the data.Model the data.Evaluate the model.Answer the problem. | |
C6928 | — On the difficulty of training Recurrent Neural Networks, 2013. Gradient clipping involves forcing the gradient values (element-wise) to a specific minimum or maximum value if the gradient exceeded an expected range. Together, these methods are often simply referred to as “gradient clipping.” | |
C6929 | There are several mathematical tools for measuring the statistical randomness of a series. The true randomness of a single event cannot be measured, but it can be investigated by looking at its history. A single event is random, if it's unintentional, no-one has decided it. | |
C6930 | 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. | |
C6931 | We see that machine learning can do what signal processing can, but has inherently higher complexity, with the benefit of being generalizable to different problems. The signal processing algorithms are optimal for the job in terms of complexity, but are specific to the particular problems they solve. | |
C6932 | The difference between data analysis and data mining is that data analysis is used to test models and hypotheses on the dataset, e.g., analyzing the effectiveness of a marketing campaign, regardless of the amount of data; in contrast, data mining uses machine learning and statistical models to uncover clandestine or | |
C6933 | In multivariate regression there are more than one dependent variable with different variances (or distributions). But when we say multiple regression, we mean only one dependent variable with a single distribution or variance. The predictor variables are more than one. | |
C6934 | A type 1 error is also known as a false positive and occurs when a researcher incorrectly rejects a true null hypothesis. The probability of making a type I error is represented by your alpha level (α), which is the p-value below which you reject the null hypothesis. | |
C6935 | Despite having similar aims and processes, there are two main differences between them: Machine learning works out predictions and recalibrates models in real-time automatically after design. Meanwhile, predictive analytics works strictly on “cause” data and must be refreshed with “change” data. | |
C6936 | In the context of machine learning, regularization is the process which regularizes or shrinks the coefficients towards zero. In simple words, regularization discourages learning a more complex or flexible model, to prevent overfitting. Moving on with this article on Regularization in Machine Learning. | |
C6937 | 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. | |
C6938 | The Bayesian Optimization algorithm can be summarized as follows:Select a Sample by Optimizing the Acquisition Function.Evaluate the Sample With the Objective Function.Update the Data and, in turn, the Surrogate Function.Go To 1. | |
C6939 | From Wikipedia, the free encyclopedia. In mathematics and statistics, a random number is either Pseudo-random or a number generated for, or part of, a set exhibiting statistical randomness. In common understanding, it's that all have an equal chance; conversely, none have an advantage. | |
C6940 | Convenience sampling is a type of nonprobability sampling in which people are sampled simply because they are "convenient" sources of data for researchers. In probability sampling, each element in the population has a known nonzero chance of being selected through the use of a random selection procedure. | |
C6941 | The joint behavior of two random variables X and Y is determined by the. joint cumulative distribution function (cdf):(1.1) FXY (x, y) = P(X ≤ x, Y ≤ y),where X and Y are continuous or discrete. For example, the probability. P(x1 ≤ X ≤ x2,y1 ≤ Y ≤ y2) = F(x2,y2) − F(x2,y1) − F(x1,y2) + F(x1,y1). | |
C6942 | Recognizing patterns allows us to predict and expect what is coming. The process of pattern recognition involves matching the information received with the information already stored in the brain. Making the connection between memories and information perceived is a step of pattern recognition called identification. | |
C6943 | Nonprobability sampling is a common technique in qualitative research where researchers use their judgment to select a sample. In convenience sampling, participants are selected because they are accessible and therefore relatively easy for the researcher to recruit. | |
C6944 | Here's a step-by-step guide to help you get started.Create a text classifier. Select 'Topic Classification' Upload your training data. Create your tags. Train your classifier. Change to Naive Bayes. Test your Naive Bayes classifier. Start working with your model. | |
C6945 | The variance (symbolized by S2) and standard deviation (the square root of the variance, symbolized by S) are the most commonly used measures of spread. We know that variance is a measure of how spread out a data set is. It is calculated as the average squared deviation of each number from the mean of a data set. | |
C6946 | For digital signature applications, the security strength of a hash function is normally its collision resistance strength. When appropriate processing is applied to the data before it is hashed, the security strength may be more than the collision resistance strength (see Section 5.2. 3). | |
C6947 | A regression model that uses L1 regularization technique is called Lasso Regression and model which uses L2 is called Ridge Regression. The key difference between these two is the penalty term. Ridge regression adds “squared magnitude” of coefficient as penalty term to the loss function. | |
C6948 | 7:5214:07Suggested clip · 100 secondsHow to Select the Correct Predictive Modeling Technique | Machine YouTubeStart of suggested clipEnd of suggested clip | |
C6949 | Different Types of Clustering AlgorithmDistribution based methods.Centroid based methods.Connectivity based methods.Density Models.Subspace clustering.Improverd By: Pragya vidyarthi. | |
C6950 | If the signal is present the person can decide that it is present or absent. These outcomes are called hits and misses. If the signal is absent the person can still decide that the signal is either present or absent. These are called false alarms or correct rejections (CR) respectively. | |
C6951 | Advantages and Disadvantages of various CPU scheduling algorithmsThe process with less execution time suffer i.e. waiting time is often quite long.Favors CPU Bound process then I/O bound process.Here, first process will get the CPU first, other processes can get CPU only after the current process has finished it's execution. This effect results in lower CPU and device utilization.More items• | |
C6952 | A representative sample is a subset of a population that seeks to accurately reflect the characteristics of the larger group. For example, a classroom of 30 students with 15 males and 15 females could generate a representative sample that might include six students: three males and three females. | |
C6953 | Dual Booting Can Impact Disk Swap Space. In most cases there shouldn't be too much impact on your hardware from dual booting. Both Linux and Windows use chunks of the hard disk drive to improve performance while the computer is running. | |
C6954 | August 2017) (Learn how and when to remove this template message) In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. | |
C6955 | A classification is a useful tool for anyone developing statistical surveys. It is a framework which both simplifies the topic being studied and makes it easy to categorise all data or responses received. | |
C6956 | The joint behavior of two random variables X and Y is determined by the. joint cumulative distribution function (cdf):(1.1) FXY (x, y) = P(X ≤ x, Y ≤ y),where X and Y are continuous or discrete. For example, the probability. P(x1 ≤ X ≤ x2,y1 ≤ Y ≤ y2) = F(x2,y2) − F(x2,y1) − F(x1,y2) + F(x1,y1). | |
C6957 | The value of the z-score tells you how many standard deviations you are away from the mean. A positive z-score indicates the raw score is higher than the mean average. For example, if a z-score is equal to +1, it is 1 standard deviation above the mean. A negative z-score reveals the raw score is below the mean average. | |
C6958 | Fully Convolutional Networks (FCNs) owe their name to their architecture, which is built only from locally connected layers, such as convolution, pooling and upsampling. Note that no dense layer is used in this kind of architecture. This reduces the number of parameters and computation time. | |
C6959 | The interquartile range is the difference between the third quartile and the first quartile in a data set, giving the middle 50%. The interquartile range is a measure of spread; it's used to build box plots, determine normal distributions and as a way to determine outliers. | |
C6960 | Logic, as per the definition of the Oxford dictionary, is "the reasoning conducted or assessed according to strict principles and validity". In Artificial Intelligence also, it carries somewhat the same meaning. Logic can be defined as the proof or validation behind any reason provided. | |
C6961 | if p is a statement variable, the negation of p is "not p", denoted by ~p. If p is true, then ~p is false. Conjunction: if p and q are statement variables, the conjunction of p and q is "p and q", denoted p q.(p q) ~(p q) p xor qExclusive Orp ~(~p)Double Negation | |
C6962 | In machine learning, a deep belief network (DBN) is a generative graphical model, or alternatively a class of deep neural network, composed of multiple layers of latent variables ("hidden units"), with connections between the layers but not between units within each layer. | |
C6963 | The main goal of randomized trials is therefore to assure that each individual has an equal probability to be assigned to one or the other treatment. Randomization also allows to balance known and unknown confounders in order to make control and treatment groups as balanced as possible. | |
C6964 | A sampling frame is a list of all the items in your population. It's a complete list of everyone or everything you want to study. The difference between a population and a sampling frame is that the population is general and the frame is specific. | |
C6965 | In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables. | |
C6966 | There are several approaches to avoiding overfitting in building decision trees.Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set.Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree. | |
C6967 | 'Learning to learn' is the ability to pursue and persist in learning, to organise one's own learning, including through effective management of time and information, both individually and in groups. | |
C6968 | Mini-Max algorithm uses recursion to search through the game-tree. Min-Max algorithm is mostly used for game playing in AI. | |
C6969 | One major disadvantage of non-probability sampling is that it's impossible to know how well you are representing the population. Plus, you can't calculate confidence intervals and margins of error. | |
C6970 | 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. | |
C6971 | Interpolation refers to using the data in order to predict data within the dataset. Extrapolation is the use of the data set to predict beyond the data set. | |
C6972 | There is a broad range of opportunities to study optimization problems that cannot be solved with an exact algorithm. This work proposes the use of neural networks such as heuristics to resolve optimization problems in those cases where the use of linear programming or Lagrange multipliers is not feasible. | |
C6973 | “Covariance” indicates the direction of the linear relationship between variables. “Correlation” on the other hand measures both the strength and direction of the linear relationship between two variables. Correlation is a function of the covariance. | |
C6974 | In the variational autoencoder model, there are only local latent variables (no datapoint shares its latent z with the latent variable of another datapoint). So we can decompose the ELBO into a sum where each term depends on a single datapoint. | |
C6975 | Cross-sectional data are the result of a data collection, carried out at a single point in time on a statistical unit. With cross-sectional data, we are not interested in the change of data over time, but in the current, valid opinion of the respondents about a question in a survey. | |
C6976 | Statistical Methods for Finding the Best Regression ModelAdjusted R-squared and Predicted R-squared: Generally, you choose the models that have higher adjusted and predicted R-squared values. P-values for the predictors: In regression, low p-values indicate terms that are statistically significant.More items• | |
C6977 | How to Choose an Optimal Learning Rate for Gradient DescentChoose a Fixed Learning Rate. The standard gradient descent procedure uses a fixed learning rate (e.g. 0.01) that is determined by trial and error. Use Learning Rate Annealing. Use Cyclical Learning Rates. Use an Adaptive Learning Rate. References. | |
C6978 | Count data models have a dependent variable that is counts (0, 1, 2, 3, and so on). Most of the data are concentrated on a few small discrete values. Examples include: the number of children a couple has, the number of doctors visits per year a person makes, and the number of trips per month that a person takes. | |
C6979 | The main difference is obviously that, in a first order reaction, the order of reaction is one by nature. A pseudo first-order reaction is second order reaction by nature but has been altered to make it a first order reaction. | |
C6980 | It is a criterion under which a hypothesis tester decides whether a given hypothesis must be accepted or rejected. The general rule of thumb is that if the value of test statics is greater than the critical value then the null hypothesis is rejected in the favor of the alternate hypothesis. | |
C6981 | Jeff Heaton (see page 158 of the linked text), who states that one hidden layer allows a neural network to approximate any function involving “a continuous mapping from one finite space to another.” With two hidden layers, the network is able to “represent an arbitrary decision boundary to arbitrary accuracy.” | |
C6982 | The factorial ANOVA should be used when the research question asks for the influence of two or more independent variables on one dependent variable. | |
C6983 | In physics, a partition function describes the statistical properties of a system in thermodynamic equilibrium. Partition functions are functions of the thermodynamic state variables, such as the temperature and volume. | |
C6984 | A Hash Collision Attack is an attempt to find two input strings of a hash function that produce the same hash result. If two separate inputs produce the same hash output, it is called a collision. | |
C6985 | Once you have generated a prediction model (also called training a model), you can put it to use making predictions. The scoring process examines a dataset and predicts results for each record based on similarities to records analyzed during model training. | |
C6986 | Philosophers today usually divide ethical theories into three general subject areas: metaethics, normative ethics, and applied ethics. Metaethics investigates where our ethical principles come from, and what they mean. | |
C6987 | The coefficient for a term represents the change in the mean response associated with a change in that term, while the other terms in the model are held constant. The sign of the coefficient indicates the direction of the relationship between the term and the response. | |
C6988 | To analyze data and reporting speed AI can be very helpful in improving the data analyzing speed and also to increase the reporting time. The data are analyzed more accurately and the reporting time is also increased. AI can be used to analyze large amounts of data to draw conclusive reports. | |
C6989 | The first variable in the binomial formula, n, stands for the number of times the experiment runs. The second variable, p, represents the probability of one specific outcome. | |
C6990 | Answer. The low value of loss function determines whether a model is a good fit for the datasets. | |
C6991 | The set of all the possible outcomes is called the sample space of the experiment and is usually denoted by S. Any subset E of the sample space S is called an event. | |
C6992 | Brief Description. The Fourier Transform is an important image processing tool which is used to decompose an image into its sine and cosine components. The output of the transformation represents the image in the Fourier or frequency domain, while the input image is the spatial domain equivalent. | |
C6993 | The only difference between a frequency histogram and a relative frequency histogram is that the vertical axis uses relative or proportional frequency instead of simple frequency (see Figure 1). | |
C6994 | Three reasons that you should NOT use deep learning(1) It doesn't work so well with small data. To achieve high performance, deep networks require extremely large datasets. (2) Deep Learning in practice is hard and expensive. Deep learning is still a very cutting edge technique. (3) Deep networks are not easily interpreted. | |
C6995 | X and Y are independent iff fX,Y (x,y) = g(x)h(y) for all x,y for some functions g and h. Proof. If X and Y are independent then you need only take g(x) = fX(x) and h(y) = fY (y). | |
C6996 | One such step is eliminating duplicate data as discussed above. Another step is resolving any conflicting data. Sometimes, datasets will have information that conflicts with each other, so data normalization is meant to address this conflicting issue and solve it before continuing. A third step is formatting the data. | |
C6997 | Prospective studies usually have fewer potential sources of bias and confounding than retrospective studies. A retrospective study looks backwards and examines exposures to suspected risk or protection factors in relation to an outcome that is established at the start of the study. | |
C6998 | A critical value of z (Z-score) is used when the sampling distribution is normal, or close to normal. Z-scores are used when the population standard deviation is known or when you have larger sample sizes. See also: T Critical Value. | |
C6999 | K-nearest neighbor is also used in retail to detect patterns in credit card usage. Many new transaction-scrutinizing software applications use kNN algorithms to analyze register data and spot unusual patterns that indicate suspicious activity. |
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