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C5800 | ARIMA models are applied in some cases where data show evidence of non-stationarity, where an initial differencing step (corresponding to the "integrated" part of the model) can be applied one or more times to eliminate the non-stationarity. | |
C5801 | False-negative test results can happen for many reasons. One older study that tested 27 different kinds of at-home pregnancy tests found that they gave false negatives almost 48 percent of the time. | |
C5802 | If a z-score is equal to 0, it is on the mean. If a Z-Score is equal to +1, it is 1 Standard Deviation above the mean. If a z-score is equal to +2, it is 2 Standard Deviations above the mean. This means that raw score of 98% is pretty darn good relative to the rest of the students in your class. | |
C5803 | A survey is an investigation about the characteristics of a given population by means of collecting data from a sample of that population and estimating their characteristics through the systematic use of statistical methodology. | |
C5804 | Post-pruning (or just pruning) is the most common way of simplifying trees. Here, nodes and subtrees are replaced with leaves to improve complexity. Pruning can not only significantly reduce the size but also improve the classification accuracy of unseen objects. | |
C5805 | Types of Recurrent Neural NetworksBinary.Linear.Continuous-Nonlinear.Additive STM equation.Shunting STM equation.Generalized STM equation.MTM: Habituative Transmitter Gates and Depressing Synapses.LTM: Gated steepest descent learning: Not Hebbian learning.More items• | |
C5806 | We use the following formula to compute variance.Var(X) = Σ ( Xi - X )2 / N = Σ xi2 / N.N is the number of scores in a set of scores. X is the mean of the N scores. Cov(X, Y) = Σ ( Xi - X ) ( Yi - Y ) / N = Σ xiyi / N.N is the number of scores in each set of data. X is the mean of the N scores in the first data set. | |
C5807 | Eigenvalues and eigenvectors allow us to "reduce" a linear operation to separate, simpler, problems. For example, if a stress is applied to a "plastic" solid, the deformation can be dissected into "principle directions"- those directions in which the deformation is greatest. | |
C5808 | Theano is deep learning library developed by the Université de Montréal in 2007. It offers fast computation and can be run on both CPU and GPU. Theano has been developed to train deep neural network algorithms. | |
C5809 | The term PCA Color Augmentation refers to a type of data augmentation technique first mentioned in the paper titled ImageNet Classification with Deep Convolutional Neural Networks. Specifically, PCA Color Augmentation is designed to shift those values based on which values are the most present in the image. | |
C5810 | Image backup is ideal for enterprises because IT departments don't have to make back ups of all employees' computers, which definitely saves time and money. Also, in comparison with file backup, you backup everything - not only files, but also drivers, settings, in a word, everything. | |
C5811 | A Power Spectral Density (PSD) is the measure of signal's power content versus frequency. Therefore, while the power spectrum calculates the area under the signal plot using the discrete Fourier Transform, the power spectrum density assigns units of power to each unit of frequency and thus, enhances periodicities. | |
C5812 | A p-value is a measure of the probability that an observed difference could have occurred just by random chance. The lower the p-value, the greater the statistical significance of the observed difference. P-value can be used as an alternative to or in a addition to pre-selected confidence levels for hypothesis testing. | |
C5813 | The Apriori algorithm is used for mining frequent itemsets and devising association rules from a transactional database. The parameters “support” and “confidence” are used. Support refers to items' frequency of occurrence; confidence is a conditional probability. Items in a transaction form an item set. | |
C5814 | Ordinary least-square regression has no normality requirement. | |
C5815 | A logarithmic scale (or log scale) is a way of displaying numerical data over a very wide range of values in a compact way—typically the largest numbers in the data are hundreds or even thousands of times larger than the smallest numbers. | |
C5816 | Artificial intelligence can dramatically improve the efficiencies of our workplaces and can augment the work humans can do. When AI takes over repetitive or dangerous tasks, it frees up the human workforce to do work they are better equipped for—tasks that involve creativity and empathy among others. | |
C5817 | Class boundaries are the data values which separate classes. They are not part of the classes or the dataset. The lower class boundary of a class is defined as the average of the lower limit of the class in question and the upper limit of the previous class. | |
C5818 | An -dimensional vector, i.e., a vector ( , , , ) with components. In dimensions greater than or equal to two, vectors are sometimes considered synonymous with points and so n-tuples ( , , , ) are sometimes called points in n-space. | |
C5819 | In this context, a factor is still a variable, but it refers to a categorical independent variable. So you may have heard of fixed factors and random factors. Like covariates, factors in a linear model can be either control variables or important independent variables. The model uses them the same way in either case. | |
C5820 | K-Nearest Neighbour is one of the simplest Machine Learning algorithms based on Supervised Learning technique. KNN algorithm at the training phase just stores the dataset and when it gets new data, then it classifies that data into a category that is much similar to the new data. | |
C5821 | One of the newest and most effective ways to resolve the vanishing gradient problem is with residual neural networks, or ResNets (not to be confused with recurrent neural networks). ResNets refer to neural networks where skip connections or residual connections are part of the network architecture. | |
C5822 | Stratified random sampling is used when the researcher wants to highlight a specific subgroup within the population. This technique is useful in such researches because it ensures the presence of the key subgroup within the sample. This allows the researcher to sample the rare extremes of the given population. | |
C5823 | To take your first steps down the artificial intelligence career path, hiring managers will likely require that you hold at least a bachelor's degree in mathematics and basic computer technology. However, for the most part, bachelor's degrees will only get you into entry-level positions. | |
C5824 | Ensemble methods | |
C5825 | compressing a finite sequence produced by an unknown information source, telling whether a given finite sequence could have reliably been produced by a given source. | |
C5826 | The FISs have been successfully applied in several fields such as automatic control, data classification, decision analysis, expert systems, and computer vision. Fuzzy inference is the real process of mapping from a given set of input variables to an output relied upon a set of fuzzy rules. | |
C5827 | systems development life cycle | |
C5828 | something that may or does vary or change; a variable feature or factor. Mathematics, Computers. a quantity or function that may assume any given value or set of values. | |
C5829 | Summary: Population variance refers to the value of variance that is calculated from population data, and sample variance is the variance calculated from sample data. As a result both variance and standard deviation derived from sample data are more than those found out from population data. | |
C5830 | Scatterplots with a linear pattern have points that seem to generally fall along a line while nonlinear patterns seem to follow along some curve. If there is no clear pattern, then it means there is no clear association or relationship between the variables that we are studying. | |
C5831 | Below are 5 data mining techniques that can help you create optimal results.Classification Analysis. This analysis is used to retrieve important and relevant information about data, and metadata. Association Rule Learning. Anomaly or Outlier Detection. Clustering Analysis. Regression Analysis. | |
C5832 | A Seq2Seq model is a model that takes a sequence of items (words, letters, time series, etc) and outputs another sequence of items. The encoder captures the context of the input sequence in the form of a hidden state vector and sends it to the decoder, which then produces the output sequence. | |
C5833 | The Linear Regression Equation The equation has the form Y= a + bX, where Y is the dependent variable (that's the variable that goes on the Y axis), X is the independent variable (i.e. it is plotted on the X axis), b is the slope of the line and a is the y-intercept. | |
C5834 | a place where a concentration of a particular phenomenon is found. | |
C5835 | A parameter is any summary number, like an average or percentage, that describes the entire population. The population mean (the greek letter "mu") and the population proportion p are two different population parameters. For example: The population comprises all likely American voters, and the parameter is p. | |
C5836 | An intuitive idea of the general shape of the distribution can also be obtained by considering this sum of squares. Since χ2 is the sum of a set of squared values, it can never be negative. The minimum chi squared value would be obtained if each Z = 0 so that χ2 would also be 0. There is no upper limit to the χ2 value. | |
C5837 | 1:0037:30Suggested clip · 92 secondsBuild Sentiment Analysis Model from Scratch using GBM - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5838 | The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. It is also known as the Gaussian distribution and the bell curve. | |
C5839 | PCA is designed to model linear variabilities in high-dimensional data. However, many high dimensional data sets have a nonlinear nature. In these cases the high-dimensional data lie on or near a nonlinear manifold (not a linear subspace) and therefore PCA can not model the variability of the data correctly. | |
C5840 | Batch normalization makes the mean and variance of the activations of each layer independent from the values themselves. This means that the magnitude of the higher order interactions are going to be suppressed, allowing larger learning rates to be used. | |
C5841 | According to Cohen's original article, values ≤ 0 as indicating no agreement and 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement. | |
C5842 | The null hypothesis is a general statement that states that there is no relationship between two phenomenons under consideration or that there is no association between two groups. An alternative hypothesis is a statement that describes that there is a relationship between two selected variables in a study. | |
C5843 | The target variable of a dataset is the feature of a dataset about which you want to gain a deeper understanding. A supervised machine learning algorithm uses historical data to learn patterns and uncover relationships between other features of your dataset and the target. | |
C5844 | How to Use GA for Optimization Problems?Generate the initial population randomly.Select the initial solution with the best fitness values.Recombine the selected solutions using mutation and crossover operators.Insert offspring into the population.More items | |
C5845 | Linear models, generalized linear models, and nonlinear models are examples of parametric regression models because we know the function that describes the relationship between the response and explanatory variables. If the relationship is unknown and nonlinear, nonparametric regression models should be used. | |
C5846 | Decision tree learning is a supervised machine learning technique for inducing a decision tree from training data. A decision tree (also referred to as a classification tree or a reduction tree) is a predictive model which is a mapping from observations about an item to conclusions about its target value. | |
C5847 | Strengths and weaknesses of correlationStrengths:WeaknessesCalculating the strength of a relationship between variables.Cannot assume cause and effect, strong correlation between variables may be misleading.1 more row | |
C5848 | A statistical project is the process of answering a research question using statistical techniques and presenting the work in a written report. The research question may arise from any field of scientific endeavor, such as athletics, advertising, aerodynamics, or nutrition. | |
C5849 | The formula for the Conditional Probability of an event can be derived from Multiplication Rule 2 as follows:Start with Multiplication Rule 2.Divide both sides of equation by P(A).Cancel P(A)s on right-hand side of equation.Commute the equation.We have derived the formula for conditional probability. | |
C5850 | Lasso regression performs L1 regularization, which adds a penalty equal to the absolute value of the magnitude of coefficients. On the other hand, L2 regularization (e.g. Ridge regression) doesn't result in elimination of coefficients or sparse models. This makes the Lasso far easier to interpret than the Ridge. | |
C5851 | Information provides a way to quantify the amount of surprise for an event measured in bits. Entropy provides a measure of the average amount of information needed to represent an event drawn from a probability distribution for a random variable. | |
C5852 | Currently AI is Used is Following Things/Fields: Autonomous Flying. Retail, Shopping and Fashion. Security and Surveillance. Sports Analytics and Activities. | |
C5853 | Pick a value for the learning rate α. The learning rate determines how big the step would be on each iteration. If α is very small, it would take long time to converge and become computationally expensive. If α is large, it may fail to converge and overshoot the minimum. | |
C5854 | Stochastic Gradient Descent (SGD) is a simple yet very efficient approach to fitting linear classifiers and regressors under convex loss functions such as (linear) Support Vector Machines and Logistic Regression. | |
C5855 | today announced the development of "Wide Learning," a machine learning technology capable of accurate judgements even when operators cannot obtain the volume of data necessary for training. | |
C5856 | In the design of experiments and analysis of variance, a main effect is the effect of an independent variable on a dependent variable averaged across the levels of any other independent variables. Main effects are essentially the overall effect of a factor. | |
C5857 | Overview. Feature columns are used to specify how Tensors received from the input function should be combined and transformed before entering the model. | |
C5858 | The main difference between probability and likelihood is that the former is normalized. Probability refers to the occurrence of future events, while a likelihood refers to past events with known outcomes. Probability is used when describing a function of the outcome given a fixed parameter value. | |
C5859 | Ordinal logistic regression (often just called 'ordinal regression') is used to predict an ordinal dependent variable given one or more independent variables. As with other types of regression, ordinal regression can also use interactions between independent variables to predict the dependent variable. | |
C5860 | Bootstrap Aggregating is an ensemble method. First, we create random samples of the training data set with replacment (sub sets of training data set). Then, we build a model (classifier or Decision tree) for each sample. Finally, results of these multiple models are combined using average or majority voting. | |
C5861 | Here are some tips for connecting the shape of a histogram with the mean and median:If the histogram is skewed right, the mean is greater than the median. If the histogram is close to symmetric, then the mean and median are close to each other. If the histogram is skewed left, the mean is less than the median. | |
C5862 | 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. | |
C5863 | Multinomial logistic regression (often just called 'multinomial regression') is used to predict a nominal dependent variable given one or more independent variables. It is sometimes considered an extension of binomial logistic regression to allow for a dependent variable with more than two categories. | |
C5864 | Linear regression is supervised. You start with a dataset with a known dependent variable (label), train your model, then apply it later. You are trying to predict a real number, like the price of a house. Logistic regression is also supervised. | |
C5865 | Whereas IRR is sensitive to the ordering of ratings, IRA is sensitive to the variation in ratings or differences in rating levels. High IRR can exist with low IRA, and thus the level of reliability does not provide an indication of the level of agreement between raters. | |
C5866 | The common application of indicators is the detection of end points of titrations. The colour of an indicator alters when the acidity or the oxidizing strength of the solution, or the concentration of a certain chemical species, reaches a critical range of values. | |
C5867 | The random forest combines hundreds or thousands of decision trees, trains each one on a slightly different set of the observations, splitting nodes in each tree considering a limited number of the features. The final predictions of the random forest are made by averaging the predictions of each individual tree. | |
C5868 | “Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However, it is mostly used in classification problems. Support Vectors are simply the co-ordinates of individual observation. | |
C5869 | The Discrete Fourier Transform is always periodic, but we usually focus only on what's inside the Nyquist window and ignore the periodic copies outside. The regular Fourier Transform (function of continuous frequency) cannot be periodic, to my knowledge. | |
C5870 | The random forest is a model made up of many decision trees. Rather than just simply averaging the prediction of trees (which we could call a “forest”), this model uses two key concepts that gives it the name random: Random sampling of training data points when building trees. | |
C5871 | Sequence-to-sequence learning (Seq2Seq) is about training models to convert sequences from one domain (e.g. sentences in English) to sequences in another domain (e.g. the same sentences translated to French). | |
C5872 | 5 ways to deal with outliers in dataSet up a filter in your testing tool. Even though this has a little cost, filtering out outliers is worth it. Remove or change outliers during post-test analysis. Change the value of outliers. Consider the underlying distribution. Consider the value of mild outliers. | |
C5873 | Bias is stated as a penchant that prevents objective consideration of an issue or situation; basically the formation of opinion beforehand without any examination. Selection is stated as the act of choosing or selecting a preference; resulting in a carefully chosen and representative choice. | |
C5874 | Best Data Visualization Techniques for small and large dataBar Chart. Bar charts are used for comparing the quantities of different categories or groups. Pie and Donut Charts. Histogram Plot. Scatter Plot. Visualizing Big Data. Box and Whisker Plot for Large Data. Word Clouds and Network Diagrams for Unstructured Data. Correlation Matrices. | |
C5875 | Filtering is a technique for modifying or enhancing an image. Linear filtering is filtering in which the value of an output pixel is a linear combination of the values of the pixels in the input pixel's neighborhood. This section discusses linear filtering in MATLAB and the Image Processing Toolbox. | |
C5876 | Approach –Load dataset from source.Split the dataset into “training” and “test” data.Train Decision tree, SVM, and KNN classifiers on the training data.Use the above classifiers to predict labels for the test data.Measure accuracy and visualise classification. | |
C5877 | The bag-of-words model is a simplifying representation used in natural language processing and information retrieval (IR). In this model, a text (such as a sentence or a document) is represented as the bag (multiset) of its words, disregarding grammar and even word order but keeping multiplicity. | |
C5878 | The mean squared error tells you how close a regression line is to a set of points. It does this by taking the distances from the points to the regression line (these distances are the “errors”) and squaring them. The squaring is necessary to remove any negative signs. It also gives more weight to larger differences. | |
C5879 | Some of the most popular methods for outlier detection are:Z-Score or Extreme Value Analysis (parametric)Probabilistic and Statistical Modeling (parametric)Linear Regression Models (PCA, LMS)Proximity Based Models (non-parametric)Information Theory Models.More items | |
C5880 | The Sobel filter is used for edge detection. It works by calculating the gradient of image intensity at each pixel within the image. It finds the direction of the largest increase from light to dark and the rate of change in that direction. | |
C5881 | Discussion ForumQue.Which search implements stack operation for searching the states?a.Depth-limited searchb.Depth-first searchc.Breadth-first searchd.None of the mentioned1 more row | |
C5882 | Univariate statistics summarize only one variable at a time. Bivariate statistics compare two variables. Multivariate statistics compare more than two variables. | |
C5883 | Logistic regression works like ordinary least squares regression but on the logit of the dependent variable. Discriminant analysis is really used only for categorization. Logistic regression is often used when we aren't even interested in categorization but in getting the odds ratios for each variable. | |
C5884 | The logistic function was discovered anew in 1920 by Pearl and Reed in a study of the population growth of the United States. They were unaware of Verhulst's work (though not of the curves for autocatalytic reactions dis0 cussed presently), and they arrived independently at the logistic curve of (10). | |
C5885 | The significance level is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. | |
C5886 | Simply put, your model becomes more complex, and less explainable. | |
C5887 | In short, it ensures each subgroup within the population receives proper representation within the sample. As a result, stratified random sampling provides better coverage of the population since the researchers have control over the subgroups to ensure all of them are represented in the sampling. | |
C5888 | The matrix of features is a term used in machine learning to describe the list of columns that contain independent variables to be processed, including all lines in the dataset. These lines in the dataset are called lines of observation. | |
C5889 | We maximize the likelihood because we maximize fit of our model to data under an implicit assumption that the observed data are at the same time most likely data. | |
C5890 | An eigenfunction of an operator is a function such that the application of on gives. again, times a constant. (49) where k is a constant called the eigenvalue. It is easy to show that if is a linear operator with an eigenfunction , then any multiple of is also an eigenfunction of . | |
C5891 | The population distribution gives the values of the variable for all the individuals in the population. The sampling distribution shows the statistic values from all the possible samples of the same size from the population. It is a distribution of the statistic. | |
C5892 | The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image. | |
C5893 | In Chi-Square goodness of fit test, the term goodness of fit is used to compare the observed sample distribution with the expected probability distribution. Chi-Square goodness of fit test determines how well theoretical distribution (such as normal, binomial, or Poisson) fits the empirical distribution. | |
C5894 | Convolution is the process of adding each element of the image to its local neighbors, weighted by the kernel. This is related to a form of mathematical convolution. The matrix operation being performed—convolution—is not traditional matrix multiplication, despite being similarly denoted by *. | |
C5895 | The ability to detect certain types of stimuli, like movements, shape, and angles, requires specialized cells in the brain called feature detectors. Without these, it would be difficult, if not impossible, to detect a round object, like a baseball, hurdling toward you at 90 miles per hour. | |
C5896 | Bayes' Theorem has many applications in areas such as mathematics, medicine, finance, marketing, engineering and many other. This paper covers Bayes' Theorem at a basic level and explores how the formula was derived. We also, look at some extended forms of the formula and give an explicit example. | |
C5897 | Exploratory Data Analysis refers to the critical process of performing initial investigations on data so as to discover patterns,to spot anomalies,to test hypothesis and to check assumptions with the help of summary statistics and graphical representations. | |
C5898 | Dummy variables (sometimes called indicator variables) are used in regression analysis and Latent Class Analysis. As implied by the name, these variables are artificial attributes, and they are used with two or more categories or levels. | |
C5899 | Backward elimination (or backward deletion) is the reverse process. All the independent variables are entered into the equation first and each one is deleted one at a time if they do not contribute to the regression equation. Stepwise selection is considered a variation of the previous two methods. |
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