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C5300 | Character N-grams (of at least 3 characters) that are common to words meaning “transport” in the same texts sample in French, Spanish and Greek and their respective frequency. | |
C5301 | “Bias in AI” refers to situations where machine learning-based data analytics systems discriminate against particular groups of people. This discrimination usually follows our own societal biases regarding race, gender, biological sex, nationality, or age (more on this later). | |
C5302 | The “regular” normal distribution has one random variable; A bivariate normal distribution is made up of two independent random variables. The two variables in a bivariate normal are both are normally distributed, and they have a normal distribution when both are added together. | |
C5303 | The key difference between time series and panel data is that time series focuses on a single individual at multiple time intervals while panel data (or longitudinal data) focuses on multiple individuals at multiple time intervals. Fields such as Econometrics and statistics relies on data. | |
C5304 | The optimal number of clusters can be defined as follow: Compute clustering algorithm (e.g., k-means clustering) for different values of k. For instance, by varying k from 1 to 10 clusters. For each k, calculate the total within-cluster sum of square (wss). | |
C5305 | A method for the off-line recognition of cursive handwriting based on hidden Markov models (HMMs) is described. The features used in the HMMs are based on the arcs of skeleton graphs of the words to be recognized. An algorithm is applied to the skeleton graph of a word that extracts the edges in a particular order. | |
C5306 | The population median is the value of the 50th percentile of some variable for all the members of the population. When members of the population are sorted by this value, the median is the middle value. | |
C5307 | Quantization is the concept that a physical quantity can have only certain discrete values. For example, matter is quantized because it is composed of individual particles that cannot be subdivided; it is not possible to have half an electron. Also, the energy levels of electrons in atoms are quantized. | |
C5308 | Transfer learning is the reuse of a pre-trained model on a new problem. It's currently very popular in deep learning because it can train deep neural networks with comparatively little data. | |
C5309 | Scientists use observation to collect and record data, which enables them to construct and then test hypotheses and theories. Scientists observe in many ways – with their own senses or with tools such as microscopes, scanners or transmitters to extend their vision or hearing. | |
C5310 | Normalization should have no impact on the performance of a decision tree. It is generally useful, when you are solving a system of equations, least squares, etc, where you can have serious issues due to rounding errors. | |
C5311 | If you are studying one group, use a paired t-test to compare the group mean over time or after an intervention, or use a one-sample t-test to compare the group mean to a standard value. If you are studying two groups, use a two-sample t-test. If you want to know only whether a difference exists, use a two-tailed test. | |
C5312 | Nonetheless, they are not the same. Standard deviation is used to measure the spread of data around the mean, while RMSE is used to measure distance between some values and prediction for those values. If you use mean as your prediction for all the cases, then RMSE and SD will be exactly the same. | |
C5313 | Agents can be grouped into four classes based on their degree of perceived intelligence and capability :Simple Reflex Agents.Model-Based Reflex Agents.Goal-Based Agents.Utility-Based Agents.Learning Agent. | |
C5314 | Image annotation for deep learning is mainly done for object detection with more precision. 3D Cuboid Annotation, Semantic Segmentation, and polygon annotation are used to annotate the images using the right tool to make the objects well-defined in the image for neural network analysis in deep learning. | |
C5315 | 5:5217:59Suggested clip · 112 secondsHow to Use SPSS-Hierarchical Multiple Regression - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5316 | Dual-booting enables you to go from a powered-off state to a menu from which you can choose which operating system to load. This menu may have one, two, or even more options, and each choice loads the environment, drivers, and system necessary for the selected option. | |
C5317 | For omitted variable bias to occur, the following two conditions must exist:The omitted variable must correlate with the dependent variable.The omitted variable must correlate with at least one independent variable that is in the regression model. | |
C5318 | Latent class growth analysis (LCGA) is a special type of GMM, whereby the variance and covariance estimates for the growth factors within each class are assumed to be fixed to zero. By this assumption, all individual growth trajectories within a class are homogeneous. | |
C5319 | Data wrangling, sometimes referred to as data munging, is the process of transforming and mapping data from one "raw" data form into another format with the intent of making it more appropriate and valuable for a variety of downstream purposes such as analytics. | |
C5320 | Noisy data is a data that has relatively signal-to-noise ratio. This error is referred to as noise. Noise creates trouble for machine learning algorithms because if not trained properly, algorithms can think of noise to be a pattern and can start generalizing from it, which of course is undesirable. | |
C5321 | The precision-recall curve shows the tradeoff between precision and recall for different threshold. A high area under the curve represents both high recall and high precision, where high precision relates to a low false positive rate, and high recall relates to a low false negative rate. | |
C5322 | Key TakeawaysΔ=b2−4ac Δ = b 2 − 4 a c is the formula for a quadratic function 's discriminant.If Δ is greater than zero, the polynomial has two real, distinct roots.If Δ is equal to zero, the polynomial has only one real root.If Δ is less than zero, the polynomial has no real roots, only two distinct complex roots.More items | |
C5323 | The generalized delta rule is a mathematically derived formula used to determine how to update a neural network during a (back propagation) training step. A set number of input and output pairs are presented repeatedly, in random order during the training. | |
C5324 | Robust regression is an alternative to least squares regression when data is contaminated with outliers or influential observations and it can also be used for the purpose of detecting influential observations. | |
C5325 | still images efficiently | |
C5326 | Log-likelihood is all your data run through the pdf of the likelihood (logistic function), the logarithm taken for each value, and then they are summed together. | |
C5327 | The correct interpretation of a 95% confidence interval is that "we are 95% confident that the population parameter is between X and X." | |
C5328 | Hidden Markov models are known for their applications to thermodynamics, statistical mechanics, physics, chemistry, economics, finance, signal processing, information theory, pattern recognition - such as speech, handwriting, gesture recognition, part-of-speech tagging, musical score following, partial discharges and | |
C5329 | Achieving translation invariance in Convolutional NNs: Then the max pooling layer takes the output from the convolutional layer and reduces its resolution and complexity. It does so by outputting only the max value from a grid.So the information about the exact position of the max value in the grid is discarded. | |
C5330 | The subdistribution hazard function, introduced by Fine and Gray, for a given type of event is defined as the instantaneous rate of occurrence of the given type of event in subjects who have not yet experienced an event of that type. | |
C5331 | Then we will propose a generalization to nonlinear models and also multiclass classification. In the case of multiclass classification, a typically used loss function is the Hard Loss Function [29, 36, 61], which counts the number of misclassifications: ℓ(f, z) = ℓH(f, z) = [f(x)≠y]. | |
C5332 | A 78 is pretty good, but the Navy goes by your line scores and not the overall AFQT. Get your line scores from your recruiter and see what you already qualify for. Just know that it's your most recent ASVAB score that counts, so if you do worse you're stuck with that score. | |
C5333 | Independent EventsTwo events A and B are said to be independent if the fact that one event has occurred does not affect the probability that the other event will occur.If whether or not one event occurs does affect the probability that the other event will occur, then the two events are said to be dependent. | |
C5334 | A dummy variable (binary variable) D is a variable that takes on the value 0 or 1. • Examples: EU member (D = 1 if EU member, 0 otherwise), brand (D = 1 if product has a particular brand, 0 otherwise), gender (D = 1 if male, 0 otherwise) | |
C5335 | 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. | |
C5336 | Bad Sampling. The data can be misleading due to the sampling method used to obtain data. For instance, the size and the type of sample used in any statistics play a significant role — many polls and questionnaires target certain audiences that provide specific answers, resulting in small and biased sample sizes. | |
C5337 | The Convolutional Recurrent Neural Networks is the combination of two of the most prominent neural networks. The CRNN (convolutional recurrent neural network) involves CNN(convolutional neural network) followed by the RNN(Recurrent neural networks). | |
C5338 | Mathematically test efficiency is calculated as a percentage of the number of alpha testing (in-house or on-site) defects divided by sum of a number of alpha testing and a number of beta testing (off-site) defects. | |
C5339 | Mean and Variance of a Binomial Distribution The variance of a Binomial Variable is always less than its mean. ∴ npq<np. For Maximum Variance: p=q=0.5 and σmax = n/4. | |
C5340 | A layer groups a number of neurons together. It is used for holding a collection of neurons. There will always be an input and output layer. We can have zero or more hidden layers in a neural network. The learning process of a neural network is performed with the layers. | |
C5341 | Email messages are a good example. While the actual content is unstructured, it does contain structured data such as name and email address of sender and recipient, time sent, etc. Another example is a digital photograph. | |
C5342 | Proof. If X and Y are independent then you need only take g(x) = fX(x) and h(y) = fY (y). Note When fX,Y (x,y) = g(x)h(y) for all x,y you can easily write down the marginal p.d.f.'s. h(y) for a suitable choice of C. | |
C5343 | Unlike the log odds ratio, the odds ratio is always positive. A value of 1 indicates no change. Values between 0 and less than 1 indicate a decrease in the probability of the outcome event. Values greater than 1 indicate an increase in the probability of the outcome event. | |
C5344 | datasets Which of the following function is used for loading famous iris dataset from sklearn. datasets? load_iris() Which of the following expressions can access the features of the iris dataset, shown in the below expression? from sklearn import datasets iris = datasets. load_iris() iris. | |
C5345 | Higher the AUC, better the model is at predicting 0s as 0s and 1s as 1s. By analogy, Higher the AUC, better the model is at distinguishing between patients with disease and no disease. The ROC curve is plotted with TPR against the FPR where TPR is on y-axis and FPR is on the x-axis. | |
C5346 | One of the key methodologies to improve efficiency in computational intensive tasks is to reduce the dimensions after ensuring most of the key information is maintained. It also eliminates features with strong correlation between them and reduces over-fitting. | |
C5347 | The Sarsa algorithm is an On-Policy algorithm for TD-Learning. The major difference between it and Q-Learning, is that the maximum reward for the next state is not necessarily used for updating the Q-values. | |
C5348 | Deep belief networks solve this problem by using an extra step called “pre-training”. Pre-training is done before backpropagation and can lead to an error rate not far from optimal. This puts us in the “neighborhood” of the final solution. Then we use backpropagation to slowly reduce the error rate from there. | |
C5349 | KNN is an algorithm that is useful for matching a point with its closest k neighbors in a multi-dimensional space. It can be used for data that are continuous, discrete, ordinal and categorical which makes it particularly useful for dealing with all kind of missing data. | |
C5350 | In computer science and engineering, a test vector is a set of inputs provided to a system in order to test that system. In software development, test vectors are a methodology of software testing and software verification and validation. | |
C5351 | "A discrete variable is one that can take on finitely many, or countably infinitely many values", whereas a continuous random variable is one that is not discrete, i.e. "can take on uncountably infinitely many values", such as a spectrum of real numbers. | |
C5352 | A commonly used rule says that a data point is an outlier if it is more than 1.5 ⋅ IQR 1.5\cdot \text{IQR} 1. 5⋅IQR1, point, 5, dot, start text, I, Q, R, end text above the third quartile or below the first quartile. Said differently, low outliers are below Q 1 − 1.5 ⋅ IQR \text{Q}_1-1.5\cdot\text{IQR} Q1−1. | |
C5353 | This assumption may be checked by looking at a histogram or a Q-Q-Plot. Normality can also be checked with a goodness of fit test (e.g., the Kolmogorov-Smirnov test), though this test must be conducted on the residuals themselves. Third, multiple linear regression assumes that there is no multicollinearity in the data. | |
C5354 | When two or more random variables are defined on a probability space, it is useful to describe how they vary together; that is, it is useful to measure the relationship between the variables. A common measure of the relationship between two random variables is the covariance. | |
C5355 | There is no non-parametric form of any regression. Regression means you are assuming that a particular parameterized model generated your data, and trying to find the parameters. Non-parametric tests are test that make no assumptions about the model that generated your data. | |
C5356 | An ARMA model, or Autoregressive Moving Average model, is used to describe weakly stationary stochastic time series in terms of two polynomials. The first of these polynomials is for autoregression, the second for the moving average. | |
C5357 | There are three big-picture methods to understand if a continuous and categorical are significantly correlated — point biserial correlation, logistic regression, and Kruskal Wallis H Test. The point biserial correlation coefficient is a special case of Pearson's correlation coefficient. | |
C5358 | Yes, you do need to scale the target variable. I will quote this reference: A target variable with a large spread of values, in turn, may result in large error gradient values causing weight values to change dramatically, making the learning process unstable. | |
C5359 | z = (x – μ) / σ For example, let's say you have a test score of 190. The test has a mean (μ) of 150 and a standard deviation (σ) of 25. Assuming a normal distribution, your z score would be: z = (x – μ) / σ | |
C5360 | 10 Ways to Improve Transfer of Learning. Focus on the relevance of what you're learning. Take time to reflect and self-explain. Use a variety of learning media. Change things up as often as possible. Identify any gaps in your knowledge. Establish clear learning goals. Practise generalising.More items• | |
C5361 | In multi-agent simulation systems the MAS is used as a model to simulate some real-world domain. Typical use is in domains involving many different components, interacting in diverse and complex ways and where the system-level properties are not readily inferred from the properties of the components. | |
C5362 | Linear relationships can be either positive or negative. Positive relationships have points that incline upwards to the right. As x values increase, y values increase. As x values decrease, y values decrease. | |
C5363 | The Wilcoxon signed rank test is a nonparametric test that compares the median of a set of numbers against a hypothetical median. The Wilcoxon rank sum test is a nonparametric test to compare two unmatched groups. It is equivalent to the Mann-Whitney test. The Gehan-Wilcoxon test is a method to compare survival curves. | |
C5364 | Deep learning or hierarchical learning is the part of machine learning which mainly follows the widely used concepts of a neural network. In this paper, we have used the concept of deep recurrent neural network (Deep-RNN) to train the model for a classification task. | |
C5365 | The splitting criteria for CART is MSE(mean squared error). Suppose we are doing a binary tree. the algorithm first will pick a value, and split the data into two subset. For each subset, it will calculate the , and calculate the MSE for each set separately. | |
C5366 | SYNONYMS FOR outlier 2 nonconformist, maverick; original, eccentric, bohemian; dissident, dissenter, iconoclast, heretic; outsider. | |
C5367 | In statistics, a confounder (also confounding variable, confounding factor, or lurking variable) is a variable that influences both the dependent variable and independent variable, causing a spurious association. Confounding is a causal concept, and as such, cannot be described in terms of correlations or associations. | |
C5368 | matrix: A rectangular arrangement of numbers or terms having various uses such as transforming coordinates in geometry, solving systems of linear equations in linear algebra and representing graphs in graph theory. | |
C5369 | Simple random sampling methods From this population, researchers choose random samples using two ways: random number tables and random number generator software. | |
C5370 | In addition, another reason to not initialize everything to zero is so that you get different answers. Some optimization techniques are deterministic, so if you initialize randomly, you'll get different answers each time you run it. This helps you explore the space better and avoid (other) local optima. | |
C5371 | Implementing time series ARIMABrief description about ARMA, ARIMA:Step-by-step general approach of implementing ARIMA:Step 1: Load the dataset and plot the source data. ( Step 2: Apply the Augmented Dickey Fuller Test (to confirm the stationarity of data)Step 3: Run ETS Decomposition on data (To check the seasonality in data)More items• | |
C5372 | Formal Definition of Sufficient Statistics More formally, a statistic Y is said to be a sufficient estimator for some parameter θ if the conditional distribution of Y: T(X1, X2,…,Xn) doesn't depend on θ. | |
C5373 | The concept of an abstract data type might be hard for some people to grasp, but it's really not that difficult. It does present a different way to view and act upon the data elements of your programs but once you learn the basics it's not too bad. One should not feel superior if they know data structure well. | |
C5374 | In spite of being linear, the Fourier transform is not shift invariant. In other words, a shift in the time domain does not correspond to a shift in the frequency domain. | |
C5375 | So ground truth can help fully identify objects in satellite photos. "Ground truth" means a set of measurements that is known to be much more accurate than measurements from the system you are testing. For example, suppose you are testing a stereo vision system to see how well it can estimate 3D positions. | |
C5376 | Boltzmann learning is statistical in nature, and is derived from the field of thermodynamics. It is similar to error-correction learning and is used during supervised training. Neural networks that use Boltzmann learning are called Boltzmann machines. | |
C5377 | The exponential distribution is often used to model the longevity of an electrical or mechanical device. In Example, the lifetime of a certain computer part has the exponential distribution with a mean of ten years (X∼Exp(0.1)). | |
C5378 | In statistics, a positively skewed (or right-skewed) distribution is a type of distribution in which most values are clustered around the left tail of the distribution while the right tail of the distribution is longer. | |
C5379 | 3.2 How to test for differences between samplesDecide on a hypothesis to test, often called the “null hypothesis” (H0 ). In our case, the hypothesis is that there is no difference between sets of samples. Decide on a statistic to test the truth of the null hypothesis.Calculate the statistic.Compare it to a reference value to establish significance, the P-value. | |
C5380 | This variance represents what the regression line cannot predict. It's equal to the sum of squared deviations of data points around predicted points, divided by N minus two. N is the number of data points in the scatterplot. Regression variance is based on differences between predicted data points and the mean of Y. | |
C5381 | To find the interquartile range (IQR), first find the median (middle value) of the lower and upper half of the data. These values are quartile 1 (Q1) and quartile 3 (Q3). The IQR is the difference between Q3 and Q1. | |
C5382 | 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 . The above statement is the general representation of the Bayes rule. | |
C5383 | SVM could be considered as a linear classifier, because it uses one or several hyperplanes as well as nonlinear with a kernel function (Gaussian or radial basis in BCI applications). | |
C5384 | Tensor Processing UnitDesignerGoogleIntroducedMay 2016TypeNeural network Machine learning | |
C5385 | The remember vector is usually called the forget gate. The output of the forget gate tells the cell state which information to forget by multiplying 0 to a position in the matrix. If the output of the forget gate is 1, the information is kept in the cell state. | |
C5386 | How to optimize your meta tags: A checklistCheck whether all your pages and your content have title tags and meta descriptions.Start paying more attention to your headings and how you structure your content.Don't forget to mark up your images with alt text.More items• | |
C5387 | Measuring the Accuracy of a Test The false positive rate is calculated as FP/FP+TN, where FP is the number of false positives and TN is the number of true negatives (FP+TN being the total number of negatives). The true positive rate (TPR, also called sensitivity) is calculated as TP/TP+FN. | |
C5388 | Similarity Measure Numerical measure of how alike two data objects often fall between 0 (no similarity) and 1 (complete similarity) Dissimilarity Measure Numerical measure of how different two data objects are range from 0 (objects are alike) to (objects are different) | |
C5389 | In a supervised learning model, the algorithm learns on a labeled dataset, providing an answer key that the algorithm can use to evaluate its accuracy on training data. An unsupervised model, in contrast, provides unlabeled data that the algorithm tries to make sense of by extracting features and patterns on its own. | |
C5390 | Probability is the study of random events. It is used in analyzing games of chance, genetics, weather prediction, and a myriad of other everyday events. Statistics is the mathematics we use to collect, organize, and interpret numerical data. | |
C5391 | In statistics, standardization is the process of putting different variables on the same scale. This process allows you to compare scores between different types of variables. Typically, to standardize variables, you calculate the mean and standard deviation for a variable. | |
C5392 | Estimation is a division of statistics and signal processing that determines the values of parameters through measured and observed empirical data. The process of estimation is carried out in order to measure and diagnose the true value of a function or a particular set of populations. | |
C5393 | In simple terms, a quantile is where a sample is divided into equal-sized, adjacent, subgroups (that's why it's sometimes called a “fractile“). The median cuts a distribution into two equal areas and so it is sometimes called 2-quantile. Quartiles are also quantiles; they divide the distribution into four equal parts. | |
C5394 | Precision and Recall. Precision talks about all the correct predictions out of total positive predictions. Recall means how many individuals were classified correctly out of all the actual positive individuals. | |
C5395 | Other ways of avoiding experimenter's bias include standardizing methods and procedures to minimize differences in experimenter-subject interactions; using blinded observers or confederates as assistants, further distancing the experimenter from the subjects; and separating the roles of investigator and experimenter. | |
C5396 | The probability mass function of the negative binomial distribution is. where r is the number of successes, k is the number of failures, and p is the probability of success. | |
C5397 | The number of units is a parameter in the LSTM, referring to the dimensionality of the hidden state and dimensionality of the output state (they must be equal). a LSTM comprises an entire layer. | |
C5398 | The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects. General intelligence is among the field's long-term goals. | |
C5399 | Multiply the Grand total by the Pretest probability to get the Total with disease. Compute the Total without disease by subtraction. Multiply the Total with disease by the Sensitivity to get the number of True positives. Multiply the Total without disease by the Specificity to get the number of True Negatives. |
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