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C7500 | Companies use machine learning models to make practical business decisions, and more accurate model outcomes result in better decisions. The cost of errors can be huge, but optimizing model accuracy mitigates that cost. The benefits of improving model accuracy help avoid considerable time, money, and undue stress. | |
C7501 | Gram matrix is simply the matrix of the inner product of each vector and its corresponding vectors in same. It found use in the current machine learning is due to deep learning loss where while style transferring the loss function is computed using the gram matrix. | |
C7502 | To find “q” or the studentized range statistic, refer to your table on page A-32 of your text. On the table 'k' or the number of groups is found along the top, and degrees of freedom within is down the side. | |
C7503 | Estimation, in statistics, any of numerous procedures used to calculate the value of some property of a population from observations of a sample drawn from the population. A point estimate, for example, is the single number most likely to express the value of the property. | |
C7504 | 2 Answers. Boosting is based on weak learners (high bias, low variance). Boosting reduces error mainly by reducing bias (and also to some extent variance, by aggregating the output from many models). On the other hand, Random Forest uses as you said fully grown decision trees (low bias, high variance). | |
C7505 | When you have a statistically significant interaction, reporting the main effects can be misleading. Therefore, you will need to report the simple main effects. | |
C7506 | A random effect model is a model all of whose factors represent random effects. (See Random Effects.) Such models are also called variance component models. Random effect models are often hierarchical models. A model that contains both fixed and random effects is called a mixed model. | |
C7507 | Order Statistics Definition Order statistics are sample values placed in ascending order. The study of order statistics deals with the applications of these ordered values and their functions. Let's say you had three weights: X1 = 22 kg, X2 = 44 kg, and X3 = 12 kg. | |
C7508 | • A random process is a time-varying function that assigns the outcome of a random experiment to each time instant: X(t). • For a fixed (sample path): a random process is a time varying function, e.g., a signal. | |
C7509 | Recurrent neural networks (RNN) are the state of the art algorithm for sequential data and are used by Apple's Siri and and Google's voice search. It is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for machine learning problems that involve sequential data. | |
C7510 | Recall is the true positive rate, also referred to as sensitivity, measures the probability of ground truth objects being correctly detected. | |
C7511 | When a table shows relative frequencies for different categories of a categorical variable, it is called a relative frequency table. The first table shows relative frequencies as a proportion, and the second table shows relative frequencies as a percentage. | |
C7512 | Matrix items are ideal when an item is sold with different options. The most common example is a shirt that is available in different colors and sizes but they can be used for anything that is sold with different options. Matrix items provide benefits that simplifies the process for users and improves data accuracy. | |
C7513 | Quantization of charge means that charge can take up only particular discrete values. The generally observed value of electric charge, q, of a substance is the integral multiples of e. Wherenbe the number of particles taken, e is the charge of one electron. | |
C7514 | Linear SVM is a parametric model, an RBF kernel SVM isn't, and the complexity of the latter grows with the size of the training set. So, the rule of thumb is: use linear SVMs (or logistic regression) for linear problems, and nonlinear kernels such as the Radial Basis Function kernel for non-linear problems. | |
C7515 | At the heart of this definition are three conditions, called the axioms of probability theory. Axiom 1: The probability of an event is a real number greater than or equal to 0. Axiom 2: The probability that at least one of all the possible outcomes of a process (such as rolling a die) will occur is 1. | |
C7516 | The leading explanation: signal detection theory, which at its most basic, states that the detection of a stimulus depends on both the intensity of the stimulus and the physical/psychological state of the individual. Basically, we notice things based on how strong they are and on how much we're paying attention. | |
C7517 | Designing partitions for query performanceLimit the size of each partition so that the query response time is within target.If you use horizontal partitioning, design the shard key so that the application can easily select the right partition. Consider the location of a partition. | |
C7518 | The function takes a loaded dataset as input and returns the dataset split into two subsets. Ideally, you can split your original dataset into input (X) and output (y) columns, then call the function passing both arrays and have them split appropriately into train and test subsets. | |
C7519 | Pearson's correlation coefficient is the test statistics that measures the statistical relationship, or association, between two continuous variables. It gives information about the magnitude of the association, or correlation, as well as the direction of the relationship. | |
C7520 | 3:0910:31Suggested clip · 114 secondsLoglinear Analysis in SPSS with Assumption Testing - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7521 | The Singular-Value Decomposition, or SVD for short, is a matrix decomposition method for reducing a matrix to its constituent parts in order to make certain subsequent matrix calculations simpler. For the case of simplicity we will focus on the SVD for real-valued matrices and ignore the case for complex numbers. | |
C7522 | Tips for Training Recurrent Neural NetworksAdaptive learning rate. We usually use adaptive optimizers such as Adam (Kingma14) because they can better handle the complex training dynamics of recurrent networks that plain gradient descent.Gradient clipping. Normalizing the loss. Truncated backpropagation. Long training time. Multi-step loss. | |
C7523 | It allows us to operate in the original feature space without computing the coordinates of the data in a higher dimensional space. In essence, what the kernel trick does for us is to offer a more efficient and less expensive way to transform data into higher dimensions. | |
C7524 | Humans are error-prone and biased, but that doesn't mean that algorithms are necessarily better. But these systems can be biased based on who builds them, how they're developed, and how they're ultimately used. This is commonly known as algorithmic bias. | |
C7525 | Log-loss is an appropriate performance measure when you're model output is the probability of a binary outcome. The log-loss measure considers confidence of the prediction when assessing how to penalize incorrect classification. | |
C7526 | The very first is a Box Plot. A box plot is a graphical display for describing the distribution of data. Box plots use the median and the lower and upper quartiles. An outlier can easily be detected via Box plot where any point above or below the whiskers represent an outlier. | |
C7527 | 2:585:44Suggested clip · 117 secondsC1 R: Using R to Conduct a Randomization Test - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7528 | Essentially, multivariate analysis is a tool to find patterns and relationships between several variables simultaneously. It lets us predict the effect a change in one variable will have on other variables. | |
C7529 | Association rules mining is another key unsupervised data mining method, after clustering, that finds interesting associations (relationships, dependencies) in large sets of data items. | |
C7530 | False positive. A false positive result is when PowerAI Vision labels or categorizes an image when it should not have. For example, categorizing an image of a cat as a dog. True negative. A true negative result is when PowerAI Vision correctly does not label or categorize an image. | |
C7531 | Starting TensorBoardOpen up the command prompt (Windows) or terminal (Ubuntu/Mac)Go into the project home directory.If you are using Python virtuanenv, activate the virtual environment you have installed TensorFlow in.Make sure that you can see the TensorFlow library through Python.More items• | |
C7532 | Because we use the natural exponential, we hugely increase the probability of the biggest score and decrease the probability of the lower scores when compared with standard normalization. Hence the "max" in softmax. | |
C7533 | Here is step by step on how to compute K-nearest neighbors KNN algorithm:Determine parameter K = number of nearest neighbors.Calculate the distance between the query-instance and all the training samples.Sort the distance and determine nearest neighbors based on the K-th minimum distance.More items | |
C7534 | The mean of the sum of squares (SS) is the variance of a set of scores, and the square root of the variance is its standard deviation. This simple calculator uses the computational formula SS = ΣX2 - ((ΣX)2 / N) - to calculate the sum of squares for a single set of scores. | |
C7535 | Loss functions and optimizations. Machines learn by means of a loss function. It's a method of evaluating how well specific algorithm models the given data. If predictions deviates too much from actual results, loss function would cough up a very large number. | |
C7536 | The focus will especially be on applications of stochastic processes as key technologies in various research areas, such as Markov chains, renewal theory, control theory, nonlinear theory, queuing theory, risk theory, communication theory engineering and traffic engineering. | |
C7537 | Boosting differs somewhat from bagging as it does not involve bootstrap sampling. Instead models are generated sequentially and iteratively, meaning that it is necessary to have information about model before iteration is produced. Boosting was motivated by Kearns and Valiant (1989). | |
C7538 | A normality test is used to determine whether sample data has been drawn from a normally distributed population (within some tolerance). A number of statistical tests, such as the Student's t-test and the one-way and two-way ANOVA require a normally distributed sample population. | |
C7539 | An independent event is an event in which the outcome isn't affected by another event. A dependent event is affected by the outcome of a second event. | |
C7540 | Another way researchers try to minimize selection bias is by conducting experimental studies, in which participants are randomly assigned to the study or control groups (i.e. randomized controlled studies or RCTs). However, selection bias can still occur in RCTs. | |
C7541 | ➢ 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. | |
C7542 | Feature scaling is essential for machine learning algorithms that calculate distances between data. Therefore, the range of all features should be normalized so that each feature contributes approximately proportionately to the final distance. | |
C7543 | When the data are already in digital form the 'reduction' of the data typically involves some editing, scaling, encoding, sorting, collating, and producing tabular summaries. When the observations are discrete but the underlying phenomenon is continuous then smoothing and interpolation are often needed. | |
C7544 | In the discussion above, Poisson regression coefficients were interpreted as the difference between the log of expected counts, where formally, this can be written as β = log( μx+1) – log( μx ), where β is the regression coefficient, μ is the expected count and the subscripts represent where the predictor variable, say | |
C7545 | Skip connections in deep architectures, as the name suggests, skip some layer in the neural network and feeds the output of one layer as the input to the next layers (instead of only the next one). As previously explained, using the chain rule, we must keep multiplying terms with the error gradient as we go backwards. | |
C7546 | Entries in the "Total" row and "Total" column are called marginal frequencies or the marginal distribution. Entries in the body of the table are called joint frequencies. | |
C7547 | Accuracy = (sensitivity) (prevalence) + (specificity) (1 - prevalence). The numerical value of accuracy represents the proportion of true positive results (both true positive and true negative) in the selected population. An accuracy of 99% of times the test result is accurate, regardless positive or negative. | |
C7548 | Researchers use convenience sampling not just because it is easy to use, but because it also has other research advantages. In pilot studies, convenience sample is usually used because it allows the researcher to obtain basic data and trends regarding his study without the complications of using a randomized sample. | |
C7549 | The value to be gained from taking a decision. Net gain is calculated by adding together the expected value of each outcome and deducting the costs associated with the decision. | |
C7550 | An image histogram is a type of histogram that acts as a graphical representation of the tonal distribution in a digital image. It plots the number of pixels for each tonal value. The vertical axis represents the size of the area (total number of pixels) that is captured in each one of these zones. | |
C7551 | They are continuous vs discrete distributions. A first difference is that multinomial distribution M(N,p) is discrete (it generalises binomial disrtibution) whereas Dirichlet distribution is continuous (it generalizes Beta distribution). | |
C7552 | 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. | |
C7553 | Advantages of Spiking Neural Networks Spiking neural networks are interesting for a few reasons. First, information can be transmitted using very weak signals as rate encoding is very robust to noise. Second, they bring new learning algorithms for unsupervised learning. | |
C7554 | Basically CV<10 is very good, 10-20 is good, 20-30 is acceptable, and CV>30 is not acceptable. | |
C7555 | The simplest divergence test, called the Divergence Test, is used to determine whether the sum of a series diverges based on the series's end-behavior. It cannot be used alone to determine wheter the sum of a series converges. Allow a series n that has infinitely many elements. | |
C7556 | You likely have run into the Softmax function, a wonderful activation function that turns numbers aka logits into probabilities that sum to one. Softmax function outputs a vector that represents the probability distributions of a list of potential outcomes. | |
C7557 | In other words, the value of the empirical distribution function at a given point is obtained by:counting the number of observations that are less than or equal to ;dividing the number thus obtained by the total number of observations, so as to obtain the proportion of observations that is less than or equal to . | |
C7558 | In physics, electronics, control systems engineering, and statistics, the frequency domain refers to the analysis of mathematical functions or signals with respect to frequency, rather than time. The inverse Fourier transform converts the frequency-domain function back to the time-domain function. | |
C7559 | Which intuitively says that the probability of has to be “really high”. In other words, if your value is smaller than E[X], then the upper bound of it taking that value is 1 (basically sort of an uninteresting statement, since you already knew the upper bound was 1 or greater). | |
C7560 | Linear discriminant function analysis (i.e., discriminant analysis) performs a multivariate test of differences between groups. In addition, discriminant analysis is used to determine the minimum number of dimensions needed to describe these differences. | |
C7561 | Preference bias is simply what representation(s) a supervised learning algorithm prefers. For example, a decision tree algorithm might prefer shorter, less complex trees. In other words, it is our algorithm's belief about what makes a good hypothesis. | |
C7562 | How to find accuracy of ARIMA model?Problem description: Prediction on CPU utilization. Step 1: From Elasticsearch I collected 1000 observations and exported on Python.Step 2: Plotted the data and checked whether data is stationary or not.Step 3: Used log to convert the data into stationary form.Step 4: Done DF test, ACF and PACF.More items• | |
C7563 | A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. Multi-agent systems can solve problems that are difficult or impossible for an individual agent or a monolithic system to solve. | |
C7564 | 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). It's the probability that a false alarm will be raised: that a positive result will be given when the true value is negative. | |
C7565 | Well, database normalization is the process of structuring a relational database in accordance with a series of so-called normal forms in order to reduce data redundancy and improve data integrity. In simpler terms, normalization makes sure that all of your data looks and reads the same way across all records. | |
C7566 | 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. | |
C7567 | Overfitting: Good performance on the training data, poor generliazation to other data. Underfitting: Poor performance on the training data and poor generalization to other data. | |
C7568 | The purpose of Causal Analysis and Resolution (CAR) is to identify causes of defects and other problems and take action to prevent them from occurring in the future. Introductory Notes The Causal Analysis and Resolution process area involves the following: Identifying and analyzing causes of defects and other problems. | |
C7569 | 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. | |
C7570 | A z-test is a statistical test to determine whether two population means are different when the variances are known and the sample size is large. It can be used to test hypotheses in which the z-test follows a normal distribution. A z-statistic, or z-score, is a number representing the result from the z-test. | |
C7571 | Epsilon greedy policy is a way of selecting random actions with uniform distribution from a set of available actions. This policy selects random actions in twice if the value of epsilon is 0.2. Consider a following example, There is a robot with capability to move in 4 direction. Up,down,left,right. | |
C7572 | 11 Ways to Build Your Confidence and Appear More Attractive. Always be ready to tell a good story. Demonstrate inquisitiveness. Practice good posture. Stop worrying about what people think. Eliminate negative self-talk. Smile. Learn from your mistakes without dwelling on them.More items• | |
C7573 | Use. Cluster sampling is typically used in market research. It's used when a researcher can't get information about the population as a whole, but they can get information about the clusters. For example, a researcher may be interested in data about city taxes in Florida. | |
C7574 | MSE loss is used for regression tasks. As the name suggests, this loss is calculated by taking the mean of squared differences between actual(target) and predicted values. | |
C7575 | An artificial neural network's learning rule or learning process is a method, mathematical logic or algorithm which improves the network's performance and/or training time. Usually, this rule is applied repeatedly over the network. | |
C7576 | A sampling frame is a list or other device used to define a researcher's population of interest. The sampling frame defines a set of elements from which a researcher can select a sample of the target population. | |
C7577 | Build the model on the training set and then use the test set as a holdout sample to test your trained model using the test data. Compare the predicted values with the actual values by calculating the error using measures such as the "Mean Absolute Percent Error" (MAPE) for example. | |
C7578 | At a high level, a computer cluster is a group of two or more computers, or nodes, that run in parallel to achieve a common goal. This allows workloads consisting of a high number of individual, parallelizable tasks to be distributed among the nodes in the cluster. | |
C7579 | Coming to the debate of Artificial Intelligence Vs Human Intelligence, recent AI achievements imitate human intelligence more closely than before, however, machines are still way beyond what human brains are capable of doing. Meanwhile, real-world scenarios need a holistic human approach. | |
C7580 | The training and testing error is the score that your train and test sets score using your error metrics. If your train error is low and test error high, you are likely overfitting to your train data. | |
C7581 | What I understand is hidden layers are intermediate layers between the input and the output layer. These could be of various types, For example, the convolutional layer in convnets is a hidden layer. A dense layer is a kind of hidden layer where every node is connected to every other node in the next layer. | |
C7582 | Find the F Statistic (the critical value for this test). The F statistic formula is: F Statistic = variance of the group means / mean of the within group variances. You can find the F Statistic in the F-Table. | |
C7583 | Autocorrelation can cause problems in conventional analyses (such as ordinary least squares regression) that assume independence of observations. In a regression analysis, autocorrelation of the regression residuals can also occur if the model is incorrectly specified. | |
C7584 | Standard deviation is the deviation from the mean, and a standard deviation is nothing but the square root of the variance. Mean is an average of all set of data available with an investor or company. Standard deviation used for measuring the volatility of a stock. | |
C7585 | Normalization is the process of organizing data into a related table; it also eliminates redundancy and increases the integrity which improves performance of the query. To normalize a database, we divide the database into tables and establish relationships between the tables. | |
C7586 | Definition: Stratified sampling is a type of sampling method in which the total population is divided into smaller groups or strata to complete the sampling process. The strata is formed based on some common characteristics in the population data. | |
C7587 | The latent space representation of our data contains all the important information needed to represent our original data point. This representation must then represent the features of the original data. In other words, the model learns the data features and simplifies its representation to make it easier to analyze. | |
C7588 | 11 Applications of Artificial Intelligence in Business:Chatbots: Artificial Intelligence in eCommerce: AI to Improve Workplace Communication: Human Resource Management: AI in Healthcare: Intelligent Cybersecurity: Artificial Intelligence in Logistics and Supply Chain: Sports betting Industry:More items• | |
C7589 | The t-test is a method that determines whether two populations are statistically different from each other, whereas ANOVA determines whether three or more populations are statistically different from each other. | |
C7590 | The Akaike information criterion (AIC) is a mathematical method for evaluating how well a model fits the data it was generated from. In statistics, AIC is used to compare different possible models and determine which one is the best fit for the data. | |
C7591 | Random Forest Algorithm The Random Forest ML Algorithm is a versatile supervised learning algorithm that's used for both classification and regression analysis tasks. | |
C7592 | However, it is not necessary for you to learn the machine learning algorithms that are not a part of machine learning in order to learn deep learning. Instead, if you want to learn deep learning then you can go straight to learning the deep learning models if you want to. | |
C7593 | All RNNs have feedback loops in the recurrent layer. This lets them maintain information in 'memory' over time. LSTM networks are a type of RNN that uses special units in addition to standard units. LSTM units include a 'memory cell' that can maintain information in memory for long periods of time. | |
C7594 | It turns out that it is easy to calculate the expected number of errors: it is the sum of the error probabilities. The most probable number of errors (E*) is also easy to calculate. First calculate E = expected errors = sum P_e. Then round down to the nearest integer, and this is the most probable number of errors. | |
C7595 | In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan used to generate a decision tree from a dataset. ID3 is the precursor to the C4. 5 algorithm, and is typically used in the machine learning and natural language processing domains. | |
C7596 | Expert System is an application using AI to build a knowledge base and use that knowledge base to solve such problems where human experts are needed to solve the problem. Artificial Intelligence targets to make machines intelligent. Expert System is an application using Artificial Intelligence. | |
C7597 | A Convolutional Neural Network (ConvNet/CNN) is a Deep Learning algorithm which can take in an input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. | |
C7598 | Chebyshev's inequality says that at least 1−1K2 of data from a sample must fall within K standard deviations from the mean, where K is any positive real number greater than one. | |
C7599 | Abstract. Dunn's test is the appropriate nonparametric pairwise multiple- comparison. procedure when a Kruskal–Wallis test is rejected, and it is now im- plemented for Stata in the dunntest command. dunntest produces multiple com- parisons following a Kruskal–Wallis k-way test by using Stata's built-in kwallis command. |
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