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C8600 | Cross-validation is a standard tool in analytics and is an important feature for helping you develop and fine-tune data mining models. Cross-validation has the following applications: Validating the robustness of a particular mining model. Evaluating multiple models from a single statement. | |
C8601 | Bayesian Optimization is an approach that uses Bayes Theorem to direct the search in order to find the minimum or maximum of an objective function. It is an approach that is most useful for objective functions that are complex, noisy, and/or expensive to evaluate. | |
C8602 | If the weights are zero, complexity of the whole deep net would be the same as that of a single neuron and the predictions would be nothing better than random. Nodes that are side-by-side in a hidden layer connected to the same inputs must have different weights for the learning algorithm to update the weights. | |
C8603 | Physical scientists often use the term root-mean-square as a synonym for standard deviation when they refer to the square root of the mean squared deviation of a signal from a given baseline or fit. | |
C8604 | Another view however is that the parameter value used to generate the data that are obtained in your study is just one drawn parameter value, where the draw is from some distribution (the prior). as parameters, but rather as random or latent effects. | |
C8605 | Two types of statistical methods are used in analyzing data: descriptive statistics and inferential statistics. Descriptive statistics are used to synopsize data from a sample exercising the mean or standard deviation. Inferential statistics are used when data is viewed as a subclass of a specific population. | |
C8606 | Let A and G be the Arithmetic Means and Geometric Means respectively of two positive numbers a and b. Then, As, a and b are positive numbers, it is obvious that A > G when G = -√ab. This proves that the Arithmetic Mean of two positive numbers can never be less than their Geometric Means. | |
C8607 | Pearson correlation (r) is used to measure strength and direction of a linear relationship between two variables. Mathematically this can be done by dividing the covariance of the two variables by the product of their standard deviations. The value of r ranges between -1 and 1. | |
C8608 | To calculate a z-score, subtract the mean from the raw score and divide that answer by the standard deviation. (i.e., raw score =15, mean = 10, standard deviation = 4. Therefore 15 minus 10 equals 5. 5 divided by 4 equals 1.25. | |
C8609 | M = mean( A ) returns the mean of the elements of A along the first array dimension whose size does not equal 1.If A is a vector, then mean(A) returns the mean of the elements.If A is a matrix, then mean(A) returns a row vector containing the mean of each column.More items | |
C8610 | Multimodal machine learning is a vibrant multi-disciplinary research field which addresses some of the original goals of artificial intelligence by integrating and modeling multiple communicative modalities, including linguistic, acoustic and visual messages. | |
C8611 | When someone talks about AR, they are referring to technology that overlays information and virtual objects on real-world scenes in real-time. It uses the existing environment and adds information to it to make a new artificial environment. | |
C8612 | The purpose of such selection is to determine a set of variables that will provide the best fit for the model so that accurate predictions can be made. Variable selection is one of the most difficult aspects of model building. | |
C8613 | When we control for variables that have a postive correlation with both the independent and the dependent variable, the original relationship will be pushed down, and become more negative. The same is true if we control for a variable that has a negative correlation with both independent and dependent. | |
C8614 | Binomial counts successes in a fixed number of trials, while Negative binomial counts failures until a fixed number successes. The Bernoulli and Geometric distributions are the simplest cases of the Binomial and Negative Binomial distributions. | |
C8615 | Paradigm: a framework containing the basic assumptions, ways of thinking, and methodology that are commonly accepted by members of a scientific community. 12. | |
C8616 | A residual neural network (ResNet) is an artificial neural network (ANN) of a kind that builds on constructs known from pyramidal cells in the cerebral cortex. Residual neural networks do this by utilizing skip connections, or shortcuts to jump over some layers. | |
C8617 | An experimental group, also known as a treatment group, receives the treatment whose effect researchers wish to study, whereas a control group does not. They should be identical in all other ways. | |
C8618 | Basically, there are three methods to solve a multi-label classification problem, namely: Problem Transformation. Adapted Algorithm.1 Binary Relevance. This is the simplest technique, which basically treats each label as a separate single class classification problem. 2 Classifier Chains. 3 Label Powerset. | |
C8619 | 6 Answers. Yes, this is the only difference. On-policy SARSA learns action values relative to the policy it follows, while off-policy Q-Learning does it relative to the greedy policy. Under some common conditions, they both converge to the real value function, but at different rates. | |
C8620 | The primary goal of EDA is to maximize the analyst's insight into a data set and into the underlying structure of a data set, while providing all of the specific items that an analyst would want to extract from a data set, such as: a good-fitting, parsimonious model. a list of outliers. | |
C8621 | The Altman Z-score is based on five financial ratios that can calculate from data found on a company's annual 10-K report. It uses profitability, leverage, liquidity, solvency, and activity to predict whether a company has a high probability of becoming insolvent. | |
C8622 | 2:1422:33Suggested clip · 114 secondsRegression Trees, Clearly Explained!!! - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C8623 | Standard deviation (represented by the symbol sigma, σ ) shows how much variation or dispersion exists from the average (mean), or expected value. More precisely, it is a measure of the average distance between the values of the data in the set and the mean. | |
C8624 | In simple random sampling, each data point has an equal probability of being chosen. Meanwhile, systematic sampling chooses a data point per each predetermined interval. While systematic sampling is easier to execute than simple random sampling, it can produce skewed results if the data set exhibits patterns. | |
C8625 | Multinomial logistic regression is used when you have a categorical dependent variable with two or more unordered levels (i.e. two or more discrete outcomes). It is practically identical to logistic regression, except that you have multiple possible outcomes instead of just one. | |
C8626 | Natural Language Processing (NLP) is what happens when computers read language. NLP processes turn text into structured data. Natural Language Generation (NLG) is what happens when computers write language. NLG processes turn structured data into text. | |
C8627 | Activation functions are mathematical equations that determine the output of a neural network. The function is attached to each neuron in the network, and determines whether it should be activated (“fired”) or not, based on whether each neuron's input is relevant for the model's prediction. | |
C8628 | A very popular technique that is used along with SGD is called Momentum. Instead of using only the gradient of the current step to guide the search, momentum also accumulates the gradient of the past steps to determine the direction to go. | |
C8629 | Regularization is a technique used for tuning the function by adding an additional penalty term in the error function. The additional term controls the excessively fluctuating function such that the coefficients don't take extreme values. | |
C8630 | Both data mining and machine learning draw from the same foundation, but in different ways. Machine learning can look at patterns and learn from them to adapt behavior for future incidents, while data mining is typically used as an information source for machine learning to pull from. | |
C8631 | High Dimensional means that the number of dimensions are staggeringly high — so high that calculations become extremely difficult. With high dimensional data, the number of features can exceed the number of observations. For example, microarrays, which measure gene expression, can contain tens of hundreds of samples. | |
C8632 | Generally, ensemble learning involves training more than one network on the same dataset, then using each of the trained models to make a prediction before combining the predictions in some way to make a final outcome or prediction. | |
C8633 | Paired means that both samples consist of the same test subjects. A paired t-test is equivalent to a one-sample t-test. Unpaired means that both samples consist of distinct test subjects. An unpaired t-test is equivalent to a two-sample t-test. | |
C8634 | Neural Networks are complex structures made of artificial neurons that can take in multiple inputs to produce a single output. Usually, a Neural Network consists of an input and output layer with one or multiple hidden layers within. | |
C8635 | UCB is a deterministic algorithm for Reinforcement Learning that focuses on exploration and exploitation based on a confidence boundary that the algorithm assigns to each machine on each round of exploration. ( A round is when a player pulls the arm of a machine) | |
C8636 | LSTMs solve the problem using a unique additive gradient structure that includes direct access to the forget gate's activations, enabling the network to encourage desired behaviour from the error gradient using frequent gates update on every time step of the learning process. | |
C8637 | A histogram shows bars representing numerical values by range of value. A bar chart shows categories, not numbers, with bars indicating the amount of each category. Histogram example: student's ages, with a bar showing the number of students in each year. | |
C8638 | Connectionism presents a cognitive theory based on simultaneously occurring, distributed signal activity via connections that can be represented numerically, where learning occurs by modifying connection strengths based on experience. | |
C8639 | For example RSA Encryption padding is randomized, ensuring that the same message encrypted multiple times looks different each time. It also avoids other weaknesses, such as encrypting the same message using different RSA keys leaking the message, or an attacker creating messages derived from some other ciphertexts. | |
C8640 | Automated machine learning (AutoML) is the process of automating the process of applying machine learning to real-world problems. AutoML covers the complete pipeline from the raw dataset to the deployable machine learning model. | |
C8641 | The most intuitive way to increase the frequency resolution of an FFT is to increase the size while keeping the sampling frequency constant. Doing this will increase the number of frequency bins that are created, decreasing the frequency difference between each. | |
C8642 | If the order doesn't matter then we have a combination, if the order do matter then we have a permutation. One could say that a permutation is an ordered combination. The number of permutations of n objects taken r at a time is determined by the following formula: P(n,r)=n! | |
C8643 | On each iteration, we update the parameters in the opposite direction of the gradient of the objective function J(w) w.r.t the parameters where the gradient gives the direction of the steepest ascent. The size of the step we take on each iteration to reach the local minimum is determined by the learning rate α. | |
C8644 | Multivariate analysis is conceptualized by tradition as the statistical study of experiments in which multiple measurements are made on each experimental unit and for which the relationship among multivariate measurements and their structure are important to the experiment's understanding. | |
C8645 | Ridge regression uses regularization with L2 norm, while Bayesian regression, is a regression model defined in probabilistic terms, with explicit priors on the parameters. The choice of priors can have the regularizing effect, e.g. using Laplace priors for coefficients is equivalent to L1 regularization. | |
C8646 | Tokenization is breaking the raw text into small chunks. Tokenization breaks the raw text into words, sentences called tokens. These tokens help in understanding the context or developing the model for the NLP. The tokenization helps in interpreting the meaning of the text by analyzing the sequence of the words. | |
C8647 | Examples of Discrete Distribution The most common discrete probability distributions include binomial, Poisson, Bernoulli, and multinomial. | |
C8648 | Numeric data types are numbers kept in database columns. Numerical data is data that is quantifiable, such as time, height, weight, amount, and so on. A non- numeric data bring up to categorical data. | |
C8649 | The classic machine learning procedure follows the scientific paradigm of induction and deduction. In the inductive step we learn the model from raw data (so called training set), and in the deductive step the model is applied to predict the behaviour of new data. | |
C8650 | In ideal conditions, facial recognition systems can have near-perfect accuracy. Verification algorithms used to match subjects to clear reference images (like a passport photo or mugshot) can achieve accuracy scores as high as 99.97% on standard assessments like NIST's Facial Recognition Vendor Test (FRVT). | |
C8651 | The input nodes take in information, in the form which can be numerically expressed. The information is presented as activation values, where each node is given a number, the higher the number, the greater the activation. The output nodes then reflect the input in a meaningful way to the outside world. | |
C8652 | Variance of estimator: Variance is one of the most popularly used measures of spread. It is taken into consideration for quantification of the amount of dispersion with respect to set of data values. Variance is defined as the average of the squared deviation of each observation from its mean. | |
C8653 | Continuous probability distribution: A probability distribution in which the random variable X can take on any value (is continuous). Because there are infinite values that X could assume, the probability of X taking on any one specific value is zero. The normal distribution is one example of a continuous distribution. | |
C8654 | Cluster analysis, in statistics, set of tools and algorithms that is used to classify different objects into groups in such a way that the similarity between two objects is maximal if they belong to the same group and minimal otherwise. | |
C8655 | 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). | |
C8656 | Introducing Social Analytics: The Easiest Way To Understand Your Social Media EngagementMeasure the success of every social message so you can re-share your most engaging content (and improve future messages).Use real data to prove the ROI of the work you do.Identify trends and understand what your audience wants.More items• | |
C8657 | I.e multicollinearity describes a linear relationship between whereas autocorrelation describes correlation of a variable with itself given a time lag. | |
C8658 | Cluster analysis tries to maximize in-group homogeneity and maximize between group heterogeneity. Multiple discriminant analysis is different. It starts with a discrete DV and tries to determine how much the levels of the IV's distinguish the members of the groups. | |
C8659 | The Skip-gram model architecture usually tries to achieve the reverse of what the CBOW model does. It tries to predict the source context words (surrounding words) given a target word (the center word). Thus the model tries to predict the context_window words based on the target_word. | |
C8660 | 5 | Problems and Issues of Linear RegressionSpecification.Proxy Variables and Measurement Error.Selection Bias.Multicollinearity.Autocorrelation.Heteroskedasticity.Simultaneous Equations.Limited Dependent Variables.More items | |
C8661 | There are two main reasons to use logarithmic scales in charts and graphs. The first is to respond to skewness towards large values; i.e., cases in which one or a few points are much larger than the bulk of the data. The second is to show percent change or multiplicative factors. | |
C8662 | An biased estimator is one which delivers an estimate which is consistently different from the parameter to be estimated. In a more formal definition we can define that the expectation E of a biased estimator is not equal to the parameter of a population. | |
C8663 | A global thresholding technique is one which makes use of a single threshold value for the whole image, whereas local thresholding technique makes use of unique threshold values for the partitioned subimages obtained from the whole image. | |
C8664 | Midrange determines the number that is halfway between the minimum and maximum numbers of a data set. It is a statistical tool that identifies a measure of center like median, mean or mode. | |
C8665 | There is a good reason why accuracy is not an appropriate measure for information retrieval problems. In almost all circumstances, the data is extremely skewed: normally over 99.9% of the documents are in the nonrelevant category. | |
C8666 | Swarm intelligence is the discipline that deals with natural and artificial systems composed of many individuals that coordinate using decentralized control and self-organization. | |
C8667 | To run the bivariate Pearson Correlation, click Analyze > Correlate > Bivariate. Select the variables Height and Weight and move them to the Variables box. In the Correlation Coefficients area, select Pearson. In the Test of Significance area, select your desired significance test, two-tailed or one-tailed. | |
C8668 | Bias can enter into algorithmic systems as a result of pre-existing cultural, social, or institutional expectations; because of technical limitations of their design; or by being used in unanticipated contexts or by audiences who are not considered in the software's initial design. | |
C8669 | How to Avoid the Type II Error?Increase the sample size. One of the simplest methods to increase the power of the test is to increase the sample size used in a test. Increase the significance level. Another method is to choose a higher level of significance. | |
C8670 | Moment generating functions have great practical relevance not only because they can be used to easily derive moments, but also because a probability distribution is uniquely determined by its mgf, a fact that, coupled with the analytical tractability of mgfs, makes them a handy tool to solve several problems, such as | |
C8671 | Panel data usually contain more degrees of freedom and more sample variability than cross-sectional data which may be viewed as a panel with T = 1, or time series data which is a panel with N = 1, hence improving the efficiency of econometric estimates (e.g. Hsiao et al., 1995). | |
C8672 | Remember that the decision to reject the null hypothesis (H 0) or fail to reject it can be based on the p-value and your chosen significance level (also called α). If the p-value is less than or equal to α, you reject H 0; if it is greater than α, you fail to reject H 0. | |
C8673 | The prior probability of an event will be revised as new data or information becomes available, to produce a more accurate measure of a potential outcome. That revised probability becomes the posterior probability and is calculated using Bayes' theorem. | |
C8674 | There are three basic concepts in reinforcement learning: state, action, and reward. The state describes the current situation. For a robot that is learning to walk, the state is the position of its two legs. For a Go program, the state is the positions of all the pieces on the board. | |
C8675 | Unstructured data is data that doesn't fit in a spreadsheet with rows and columns. It isn't in a database. Examples of unstructured data includes things like video, audio or image files, as well as log files, sensor or social media posts. | |
C8676 | Explanation: K-means requires a number of clusters. 9. | |
C8677 | In probability theory and statistics, the Bernoulli distribution, named after Swiss mathematician Jacob Bernoulli, is the discrete probability distribution of a random variable which takes the value 1 with probability and the value 0 with probability , and is sometimes denoted as . | |
C8678 | A regression tree is built through a process known as binary recursive partitioning, which is an iterative process that splits the data into partitions or branches, and then continues splitting each partition into smaller groups as the method moves up each branch. | |
C8679 | Analysis of variance (ANOVA) is an analysis tool used in statistics that splits an observed aggregate variability found inside a data set into two parts: systematic factors and random factors. The systematic factors have a statistical influence on the given data set, while the random factors do not. | |
C8680 | Logistic regression is a supervised learning classification algorithm used to predict the probability of a target variable. The nature of target or dependent variable is dichotomous, which means there would be only two possible classes. | |
C8681 | Normal distribution describes continuous data which have a symmetric distribution, with a characteristic 'bell' shape. Binomial distribution describes the distribution of binary data from a finite sample. Thus it gives the probability of getting r events out of n trials. | |
C8682 | The probability that a standard normal random variables lies between two values is also easy to find. The P(a < Z < b) = P(Z < b) - P(Z < a). For example, suppose we want to know the probability that a z-score will be greater than -1.40 and less than -1.20. | |
C8683 | Precision and recall at k: Definition Precision at k is the proportion of recommended items in the top-k set that are relevant. Its interpretation is as follows. Suppose that my precision at 10 in a top-10 recommendation problem is 80%. This means that 80% of the recommendation I make are relevant to the user. | |
C8684 | Because the coefficient of determination is the result of squaring the correlation coefficient, the coefficient of determination cannot be negative. (Even if the correlation is negative, squaring it will result in a positive number.) | |
C8685 | The Canny filter is a multi-stage edge detector. It uses a filter based on the derivative of a Gaussian in order to compute the intensity of the gradients. The Gaussian reduces the effect of noise present in the image. | |
C8686 | Longitudinal data (also known as panel data) arises when you measure a response variable of interest repeatedly through time for multiple subjects. The response variables in longitudinal studies can be either continuous or discrete. | |
C8687 | You can use the ArffViewer:(Tools -> ArffViewer or Ctrl+A). Then open your CSV file.Next go to File -> Save as and select Arff data files (should be selected by default. | |
C8688 | Backward chaining executes declare expression rules when a value is needed for a property, as opposed to when inputs change. Backward chaining applies to declare expressions rules with the Calculate Value field set to one of the following: When used if no value present. When used, if property is missing. Whenever used. | |
C8689 | Class Boundaries. Separate one class in a grouped frequency distribution from another. The boundaries have one more decimal place than the raw data and therefore do not appear in the data. There is no gap between the upper boundary of one class and the lower boundary of the next class. | |
C8690 | This is a form of regression, that constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, this technique discourages learning a more complex or flexible model, so as to avoid the risk of overfitting. A simple relation for linear regression looks like this. | |
C8691 | In summary, nominal variables are used to “name,” or label a series of values. Ordinal scales provide good information about the order of choices, such as in a customer satisfaction survey. Interval scales give us the order of values + the ability to quantify the difference between each one. | |
C8692 | When faced with a choice, we often lack the time or resources to investigate in greater depth. Faced with the need for an immediate decision, the availability heuristic allows people to quickly arrive at a conclusion. This can be helpful when you are trying to make a decision or judgment about the world around you. | |
C8693 | N-grams are simply all combinations of adjacent words or letters of length n that you can find in your source text. For example, given the word fox , all 2-grams (or “bigrams”) are fo and ox . The longer the n-gram (the higher the n), the more context you have to work with. | |
C8694 | Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. And, Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves. | |
C8695 | Mean deviation from median =∑fi∑fi∣xi−M∣=1001428. 6=14. | |
C8696 | Poisson regression – Poisson regression is often used for modeling count data. Poisson regression has a number of extensions useful for count models. Negative binomial regression – Negative binomial regression can be used for over-dispersed count data, that is when the conditional variance exceeds the conditional mean. | |
C8697 | adjective. apt or liable to vary or change; changeable: variable weather;variable moods. capable of being varied or changed; alterable: a variable time limit for completion of a book. | |
C8698 | The Bernoulli distribution represents the success or failure of a single Bernoulli trial. The Binomial Distribution represents the number of successes and failures in n independent Bernoulli trials for some given value of n. Another example is the number of heads obtained in tossing a coin n times. | |
C8699 | Explanation: Weight adjustment is proportional to negative gradient of error with respect to weight. 10. |
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