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C4500 | ➢ To determine the critical region for a normal distribution, we use the table for the standard normal distribution. If the level of significance is α = 0.10, then for a one tailed test the critical region is below z = -1.28 or above z = 1.28. | |
C4501 | Accuracy of a measured value refers to how close a measurement is to the correct value. The uncertainty in a measurement is an estimate of the amount by which the measurement result may differ from this value. Precision of measured values refers to how close the agreement is between repeated measurements. | |
C4502 | The law of averages is sometimes known as “Gambler's Fallacy. ” It evokes the idea that an event is “due” to happen. The law of averages says it's due to land on black! ” Of course, the wheel has no memory and its probabilities do not change according to past results. | |
C4503 | The feature map on CNN is the output of one filter applied to the previous layer. A filter that is given is drawn across the entire previous layer, moved one pixel at a time. Each position results in activation of the neuron and the output are collected in the feature map. | |
C4504 | Natural Language Processing (NLP) is the part of AI that studies how machines interact with human language. Combined with machine learning algorithms, NLP creates systems that learn to perform tasks on their own and get better through experience. | |
C4505 | Regression analysis is a tool for building statistical models that characterize relationships among a dependent variable and one or more independent variables, all of which are numerical. Simple linear regression involves a single independent variable. Multiple regression involves two or more independent variables. | |
C4506 | One-class SVM is an unsupervised algorithm that learns a decision function for novelty detection: classifying new data as similar or different to the training set. | |
C4507 | Classification is a data mining function that assigns items in a collection to target categories or classes. The goal of classification is to accurately predict the target class for each case in the data. For example, a classification model could be used to identify loan applicants as low, medium, or high credit risks. | |
C4508 | TensorFlow is an open source machine learning framework for carrying out high-performance numerical computations. It provides excellent architecture support which allows easy deployment of computations across a variety of platforms ranging from desktops to clusters of servers, mobiles, and edge devices. | |
C4509 | Decision tree classifier – Decision tree classifier is a systematic approach for multiclass classification. It poses a set of questions to the dataset (related to its attributes/features). The decision tree classification algorithm can be visualized on a binary tree. | |
C4510 | Classification Accuracy It is the ratio of number of correct predictions to the total number of input samples. It works well only if there are equal number of samples belonging to each class. For example, consider that there are 98% samples of class A and 2% samples of class B in our training set. | |
C4511 | These pages demonstrate how to use Moran's I or a Mantel test to check for spatial autocorrelation in your data. Moran's I is a parametric test while Mantel's test is semi-parametric. Both will also indicate if your spatial autocorrelation is positive or negative and provide a p-value for the level of autocorrelation. | |
C4512 | Top 10 Data Analytics toolsR Programming. R is the leading analytics tool in the industry and widely used for statistics and data modeling. Tableau Public: SAS: Apache Spark. Excel. RapidMiner:KNIME. QlikView.More items• | |
C4513 | The key difference between a GRU and an LSTM is that a GRU has two gates (reset and update gates) whereas an LSTM has three gates (namely input, output and forget gates). Why do we make use of GRU when we clearly have more control on the network through the LSTM model (as we have three gates)? | |
C4514 | The distribution of sample statistics is called sampling distribution. Next a new sample of sixteen is taken, and the mean is again computed. If this process were repeated an infinite number of times, the distribution of the now infinite number of sample means would be called the sampling distribution of the mean. | |
C4515 | Advantages and Disadvantages of Decision Trees in Machine Learning. Decision Tree is used to solve both classification and regression problems. But the main drawback of Decision Tree is that it generally leads to overfitting of the data. | |
C4516 | Three keys to managing bias when building AIChoose the right learning model for the problem. There's a reason all AI models are unique: Each problem requires a different solution and provides varying data resources. Choose a representative training data set. Monitor performance using real data. | |
C4517 | A major difference is in its shape: the normal distribution is symmetrical, whereas the lognormal distribution is not. Because the values in a lognormal distribution are positive, they create a right-skewed curve. A further distinction is that the values used to derive a lognormal distribution are normally distributed. | |
C4518 | This variant of hierarchical clustering is called top-down clustering or divisive clustering . We start at the top with all documents in one cluster. The cluster is split using a flat clustering algorithm. This procedure is applied recursively until each document is in its own singleton cluster. | |
C4519 | The median absolute deviation and interquartile range are robust measures of statistical dispersion, while the standard deviation and range are not. Trimmed estimators and Winsorised estimators are general methods to make statistics more robust. | |
C4520 | In probability theory and statistics, the discrete uniform distribution is a symmetric probability distribution wherein a finite number of values are equally likely to be observed; every one of n values has equal probability 1/n. A simple example of the discrete uniform distribution is throwing a fair die. | |
C4521 | Calculating Standard Error of the MeanFirst, take the square of the difference between each data point and the sample mean, finding the sum of those values.Then, divide that sum by the sample size minus one, which is the variance.Finally, take the square root of the variance to get the SD. | |
C4522 | Content-based filtering, makes recommendations based on user preferences for product features. Collaborative filtering mimics user-to-user recommendations. Content-based filtering can recommend a new item, but needs more data of user preference in order to incorporate best match. | |
C4523 | any of various neurons located in extrastriate visual areas, particularly those in the inferotemporal cortex, that respond regardless of the location of a stimulus in the receptive field. | |
C4524 | 1 (Gamma-Poisson relationship) There is an interesting relationship between the gamma and Poisson distributions. If X is a gamma(α, β) random variable, where α is an integer, then for any x, P(X ≤ x) = P(Y ≥ α), (1) where Y ∼ Poisson(x/β). There are a number of important special cases of the gamma distribution. | |
C4525 | : a group of people or things that make up a complete unit (such as a musical group, a group of actors or dancers, or a set of clothes) See the full definition for ensemble in the English Language Learners Dictionary. ensemble. noun. | |
C4526 | In machine learning, the perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function which can decide whether or not an input, represented by a vector of numbers, belongs to some specific class. | |
C4527 | Fisher's Exact Test The null hypothesis is that these two classifications are not different. The P values in this test are computed by considering all possible tables that could give the row and column totals observed. A mathematical short cut relates these permutations to factorials; a form shown in many textbooks. | |
C4528 | The most common form of pooling is max pooling. Max pooling is done to in part to help over-fitting by providing an abstracted form of the representation. As well, it reduces the computational cost by reducing the number of parameters to learn and provides basic translation invariance to the internal representation. | |
C4529 | Nonparametric regression is a category of regression analysis in which the predictor does not take a predetermined form but is constructed according to information derived from the data. That is, no parametric form is assumed for the relationship between predictors and dependent variable. | |
C4530 | In this context, correlation only makes sense if the relationship is indeed linear. Second, the slope of the regression line is proportional to the correlation coefficient: slope = r*(SD of y)/(SD of x) Third: the square of the correlation, called "R-squared", measures the "fit" of the regression line to the data. | |
C4531 | A mean can be determined for grouped data, or data that is placed in intervals. The sum of the products divided by the total number of values will be the value of the mean. | |
C4532 | The goal of cluster analysis is to obtain groupings or clusters of similar samples. This is accomplished by using a distance measure derived from the multivariate gene expression data that characterizes the ``distance'' of the patients' expression patterns with each other. | |
C4533 | Some techniques which are used in digital image processing include:Anisotropic diffusion.Hidden Markov models.Image editing.Image restoration.Independent component analysis.Linear filtering.Neural networks.Partial differential equations.More items | |
C4534 | You can use a generative model. You can also use simple tricks. For example, with photograph image data, you can get big gains by randomly shifting and rotating existing images. It improves the generalization of the model to such transforms in the data if they are to be expected in new data. | |
C4535 | An exogenous variable is a variable that is not affected by other variables in the system. For example, take a simple causal system like farming. Variables like weather, farmer skill, pests, and availability of seed are all exogenous to crop production. | |
C4536 | Categorical variables require special attention in regression analysis because, unlike dichotomous or continuous variables, they cannot by entered into the regression equation just as they are. Instead, they need to be recoded into a series of variables which can then be entered into the regression model. | |
C4537 | Energy is quantized in some systems, meaning that the system can have only certain energies and not a continuum of energies, unlike the classical case. This would be like having only certain speeds at which a car can travel because its kinetic energy can have only certain values. | |
C4538 | 1:113:06Suggested clip · 115 secondsStatistics - How to make a histogram - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C4539 | Let's Start with NLP and NLG Setting aside NLU for the moment, we can draw a really simple distinction: 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. | |
C4540 | A discrete distribution is a statistical distribution that shows the probabilities of discrete (countable) outcomes, such as 1, 2, 3 Overall, the concepts of discrete and continuous probability distributions and the random variables they describe are the underpinnings of probability theory and statistical analysis. | |
C4541 | A box and whisker plot—also called a box plot—displays the five-number summary of a set of data. The five-number summary is the minimum, first quartile, median, third quartile, and maximum. | |
C4542 | A common exercise in empirical studies is a “robustness check”, where the researcher examines how certain “core” regression coefficient estimates behave when the regression specification is modified by adding or removing regressors. | |
C4543 | Statistically significant means a result is unlikely due to chance. The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn't a difference for all users. Statistical significance doesn't mean practical significance. | |
C4544 | Observational error (or measurement error) is the difference between a measured value of a quantity and its true value. Random errors are errors in measurement that lead to measurable values being inconsistent when repeated measurements of a constant attribute or quantity are taken. | |
C4545 | In 1D CNN, kernel moves in 1 direction. Input and output data of 1D CNN is 2 dimensional. Mostly used on Time-Series data. In 2D CNN, kernel moves in 2 directions. Input and output data of 2D CNN is 3 dimensional. | |
C4546 | To apply the linear regression t-test to sample data, we require the standard error of the slope, the slope of the regression line, the degrees of freedom, the t statistic test statistic, and the P-value of the test statistic. Therefore, the P-value is 0.0121 + 0.0121 or 0.0242. Interpret results. | |
C4547 | The decision rule is: Reject H0 if Z < 1.645. The decision rule is: Reject H0 if Z < -1.960 or if Z > 1.960. The complete table of critical values of Z for upper, lower and two-tailed tests can be found in the table of Z values to the right in "Other Resources." | |
C4548 | How you do this:Count the total number of items. In this chart the total is 40.Divide the count (the frequency) by the total number. For example, 1/40 = . 025 or 3/40 = . 075. | |
C4549 | fastText is a library for efficient learning of word representations and sentence classification. In this document we present how to use fastText in python. | |
C4550 | 5 Answers. N is the population size and n is the sample size. The question asks why the population variance is the mean squared deviation from the mean rather than (N−1)/N=1−(1/N) times it. | |
C4551 | Convolutional Neural Networks | |
C4552 | 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. | |
C4553 | The sigmoid activation function This causes vanishing gradients and poor learning for deep networks. This can occur when the weights of our networks are initialized poorly – with too-large negative and positive values. It's called a rectified linear unit activation function, or ReLU. | |
C4554 | There are 5 values above the median (upper half), the middle value is 77 which is the third quartile. The interquartile range is 77 – 64 = 13; the interquartile range is the range of the middle 50% of the data. When the sample size is odd, the median and quartiles are determined in the same way. | |
C4555 | Image processing techniques use filters to enhance an image. Their main applications are to transform the contrast, brightness, resolution and noise level of an image. Contouring, image sharpening, blurring, embossing and edge detection are typical image processing functions (see Table 4.1). | |
C4556 | Standard deviation looks at how spread out a group of numbers is from the mean, by looking at the square root of the variance. The variance measures the average degree to which each point differs from the mean—the average of all data points. | |
C4557 | Markov chains are used in a broad variety of academic fields, ranging from biology to economics. When predicting the value of an asset, Markov chains can be used to model the randomness. The price is set by a random factor which can be determined by a Markov chain. | |
C4558 | A relative frequency distribution lists the data values along with the percent of all observations belonging to each group. These relative frequencies are calculated by dividing the frequencies for each group by the total number of observations. The horizontal axis represents the range of data values. | |
C4559 | Use Simple Random Sampling One of the most effective methods that can be used by researchers to avoid sampling bias is simple random sampling, in which samples are chosen strictly by chance. This provides equal odds for every member of the population to be chosen as a participant in the study at hand. | |
C4560 | In case of continous series, a mid point is computed as lower−limit+upper−limit2 and Mean Deviation is computed using following formula.FormulaN = Number of observations.f = Different values of frequency f.x = Different values of mid points for ranges.Me = Median. | |
C4561 | The coefficient of variation represents the ratio of the standard deviation to the mean, and it is a useful statistic for comparing the degree of variation from one data series to another, even if the means are drastically different from one another. | |
C4562 | While a frequency distribution gives the exact frequency or the number of times a data point occurs, a probability distribution gives the probability of occurrence of the given data point. | |
C4563 | Test error is consistently higher than training error: if this is by a small margin, and both error curves are decreasing with epochs, it should be fine. However if your test set error is not decreasing, while your training error is decreasing alot, it means you are over fitting severely. | |
C4564 | Communality value is also a deciding factor to include or exclude a variable in the factor analysis. A value of above 0.5 is considered to be ideal. But in a study, it is seen that a variable with low community value (<0.5), is contributing to a well defined factor, though loading is low. | |
C4565 | Adding more training data.Reducing parameters. We have too many neurons in our hidden layers or too many layers. Let's remove some layers, or reduce the number of hidden neurons.Increase regularization. Either by increasing our. for L1/L2 weight regularization. We can also use dropout the technique. | |
C4566 | Knowledge-representation is a field of artificial intelligence that focuses on designing computer representations that capture information about the world that can be used to solve complex problems. Virtually all knowledge representation languages have a reasoning or inference engine as part of the system. | |
C4567 | Yes you can. It is also seen that using both of them together increases the accuracy. | |
C4568 | Hashing is an algorithm that calculates a fixed-size bit string value from a file. A file basically contains blocks of data. Hashing transforms this data into a far shorter fixed-length value or key which represents the original string. | |
C4569 | Stochastic Gradient Descent (SGD) Here, the term "stochastic" comes from the fact that the gradient based on a single training sample is a "stochastic approximation" of the "true" cost gradient. | |
C4570 | Key TakeawaysThe union of two or more sets is the set that contains all the elements of the two or more sets. The general probability addition rule for the union of two events states that P(A∪B)=P(A)+P(B)−P(A∩B) P ( A ∪ B ) = P ( A ) + P ( B ) − P ( A ∩ B ) , where A∩B A ∩ B is the intersection of the two sets.More items | |
C4571 | Unsupervised learning is the training of machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. | |
C4572 | In statistics, ordinal regression (also called "ordinal classification") is a type of regression analysis used for predicting an ordinal variable, i.e. a variable whose value exists on an arbitrary scale where only the relative ordering between different values is significant. | |
C4573 | Key Takeaways. Standard deviation looks at how spread out a group of numbers is from the mean, by looking at the square root of the variance. The variance measures the average degree to which each point differs from the mean—the average of all data points. | |
C4574 | YES. we calculate height of the class interval by dividing the frequency by that class width. That class which has the maximum height will be the modal class, containing the mode. | |
C4575 | Frequency is not quantized, and has a continuous spectrum. As such, a photon can have any energy, as E=ℏω. However, quantum mechanically, if a particle is restricted by a potential, i.e. for V≠0, the energy spectrum is discrete. | |
C4576 | 4:066:40Suggested clip · 67 secondsSPSS - Factorial ANOVA, Two Independent Factors - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C4577 | Deep learning itself does feature engineering whereas machine learning requires manual feature engineering. 2) Which of the following is a representation learning algorithm? Neural network converts data in such a form that it would be better to solve the desired problem. This is called representation learning. | |
C4578 | When small samples are used to estimate a population mean, in cases where the population standard deviation is unknown: the t-distribution must be used to obtain the critical value. the resulting margin of error for a confidence interval estimate will tend to be fairly small. | |
C4579 | 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. | |
C4580 | Neural style transfer is an optimization technique used to take two images—a content image and a style reference image (such as an artwork by a famous painter)—and blend them together so the output image looks like the content image, but “painted” in the style of the style reference image. | |
C4581 | Similarity is a machine learning method that uses a nearest neighbor approach to identify the similarity of two or more objects to each other based on algorithmic distance functions. As a method, similarity is different than: Neural Networks which create vector nodes to predict an outcome. | |
C4582 | A training dataset is a dataset of examples used during the learning process and is used to fit the parameters (e.g., weights) of, for example, a classifier. | |
C4583 | A t-test is a type of inferential statistic used to determine if there is a significant difference between the means of two groups, which may be related in certain features. The t-test is one of many tests used for the purpose of hypothesis testing in statistics. Calculating a t-test requires three key data values. | |
C4584 | How to train a Machine Learning model in 5 minutesModel Naming — Give Your Model a Name: Let's start with giving your model a name, describe your model and attach tags to your model. Data Type Selection — Choose data type(Images/Text/CSV): It's time to tell us about the type of data you want to train your model.More items | |
C4585 | Feature Scaling is a technique to standardize the independent features present in the data in a fixed range. If feature scaling is not done, then a machine learning algorithm tends to weigh greater values, higher and consider smaller values as the lower values, regardless of the unit of the values. | |
C4586 | The Elbow Method is more of a decision rule, while the Silhouette is a metric used for validation while clustering. Thus, it can be used in combination with the Elbow Method. Therefore, the Elbow Method and the Silhouette Method are not alternatives to each other for finding the optimal K. | |
C4587 | SY = the standard deviation of the Y variable. SX = the standard deviation of the X variable. X bar = the mean of the X variable. Y bar = the mean of the Y variable. | |
C4588 | As an unsupervised classification technique, clustering identifies some inherent structures present in a set of objects based on a similarity measure. Clustering methods can be based on statistical model identification (McLachlan & Basford, 1988) or competitive learning. | |
C4589 | The AUC for the ROC can be calculated using the roc_auc_score() function. Like the roc_curve() function, the AUC function takes both the true outcomes (0,1) from the test set and the predicted probabilities for the 1 class. It returns the AUC score between 0.0 and 1.0 for no skill and perfect skill respectively. | |
C4590 | Construct a probability distribution: StepsStep 1: Write down the number of widgets (things, items, products or other named thing) given on one horizontal line. Step 2: Directly underneath the first line, write the probability of the event happening. | |
C4591 | The test command, when applied to a single hypothesis, produces an F- statistic with one numerator d.f. The t-statistic of which you speak is the square root of that F-statistic. Its p-value is identical to that of the F-statistic. E.g. | |
C4592 | As already discussed, SVM aims at maximizing the geometric margin and returns the corresponding hyperplane. Such points are called as support vectors (fig. - 1). Therefore, the optimization problem as defined above is equivalent to the problem of maximizing the margin value (not geometric/functional margin values). | |
C4593 | A semantic network is a graphic notation for representing knowledge in patterns of interconnected nodes. Semantic networks became popular in artificial intelligence and natural language processing only because it represents knowledge or supports reasoning. | |
C4594 | 1:2611:18Suggested clip · 118 secondsMultiple Logistic Regression in SPSS - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C4595 | Statistics draws population inferences from a sample, and machine learning finds generalizable predictive patterns. Two major goals in the study of biological systems are inference and prediction. Many methods from statistics and machine learning (ML) may, in principle, be used for both prediction and inference. | |
C4596 | verb (used with object), in·ter·po·lat·ed, in·ter·po·lat·ing. to introduce (something additional or extraneous) between other things or parts; interject; interpose; intercalate. Mathematics. to insert, estimate, or find an intermediate term in (a sequence). | |
C4597 | Characteristics of a Relationship. Correlations have three important characterstics. They can tell us about the direction of the relationship, the form (shape) of the relationship, and the degree (strength) of the relationship between two variables. | |
C4598 | Line of Best Fit | |
C4599 | Image processing is often viewed as arbitrarily manipulating an image to achieve an aesthetic standard or to support a preferred reality. However, image processing is more accurately defined as a means of translation between the human visual system and digital imaging devices. |
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