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C5600 | Predictive analytics is the use of data, statistical algorithms and machine learning techniques to identify the likelihood of future outcomes based on historical data. The goal is to go beyond knowing what has happened to providing a best assessment of what will happen in the future. | |
C5601 | A random variable is a variable whose value is unknown or a function that assigns values to each of an experiment's outcomes. Random variables are often used in econometric or regression analysis to determine statistical relationships among one another. | |
C5602 | Unlike classical (sparse, denoising, etc.) autoencoders, Variational autoencoders (VAEs) are generative models, like Generative Adversarial Networks. | |
C5603 | PDF according to input X being discrete or continuous generates probability mass functions and CDF does the same but generates cumulative mass function. That means, PDF is derivative of CDF and CDF can be applied at any point where PDF has been applied. The cumulative function is the integral of the density function. | |
C5604 | Root Mean Square Error (RMSE) is the standard deviation of the residuals (prediction errors). Residuals are a measure of how far from the regression line data points are; RMSE is a measure of how spread out these residuals are. In other words, it tells you how concentrated the data is around the line of best fit. | |
C5605 | To assess which word2vec model is best, simply calculate the distance for each pair, do it 200 times, sum up the total distance, and the smallest total distance will be your best model. | |
C5606 | Restricted Boltzmann Machines are shallow, two-layer neural nets that constitute the building blocks of deep-belief networks. The first layer of the RBM is called the visible, or input layer, and the second is the hidden layer. Each circle represents a neuron-like unit called a node. | |
C5607 | IQ, short for intelligence quotient, is a measure of a person's reasoning ability. In short, it is supposed to gauge how well someone can use information and logic to answer questions or make predictions. IQ tests begin to assess this by measuring short- and long-term memory. | |
C5608 | A Bayesian network is a compact, flexible and interpretable representation of a joint probability distribution. It is also an useful tool in knowledge discovery as directed acyclic graphs allow representing causal relations between variables. Typically, a Bayesian network is learned from data. | |
C5609 | A Fourier transform is holographic because all points in the input affect a single point in the output and vice versa. The neural nets in organic brains have been considered holographic because skills and memories seem to be spread out over many different neurons. | |
C5610 | Unimodal data has a distribution that is single-peaked (one mode). Bimodal data has two peaks (2 modes) and multimodal data refer to distributions with more than two clear peaks. | |
C5611 | 17. Deep Convolutional Network (DCN): Convolutional Neural Networks are neural networks used primarily for classification of images, clustering of images and object recognition. | |
C5612 | Steps: In tensorflow one steps is considered as number of epochs multiplied by examples divided by batch size. steps = (epoch * examples)/batch size For instance epoch = 100, examples = 1000 and batch_size = 1000 steps = 100. | |
C5613 | Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables. | |
C5614 | Since both drifts involve a statistical change in the data, the best approach to detect them is by monitoring its statistical properties, the model's predictions, and their correlation with other factors. | |
C5615 | Specifically, synchronous learning is a type of group learning where everyone learns at the same time. On the contrary, asynchronous learning is more self-directed, and the student decides the times that he will learn. TeachThought explains that, historically, online learning was asynchronous. | |
C5616 | In simple terms: Training a Neural Network means finding the appropriate Weights of the Neural Connections thanks to a feedback loop called Gradient Backward propagation … and that's it folks. | |
C5617 | Exponential Smoothing is one of the more popular smoothing techniques due to its flexibility, ease in calculation, and good performance. Exponential Smoothing uses a simple average calculation to assign exponentially decreasing weights starting with the most recent observations. | |
C5618 | Cowell says that the Gini coefficient is useful, particularly because it allows negative values for income and wealth, unlike some other measures of inequality. (If some amount of the population has negative wealth (owes money), the Lorenz curve will dip below the x-axis.) But the Gini coefficient also has limitations. | |
C5619 | The margin of error is a statistic expressing the amount of random sampling error in the results of a survey. The larger the margin of error, the less confidence one should have that a poll result would reflect the result of a survey of the entire population. | |
C5620 | The coefficient of variation (COV) is a measure of relative event dispersion that's equal to the ratio between the standard deviation and the mean. While it is most commonly used to compare relative risk, the COV may be applied to any type of quantitative likelihood or probability distribution. | |
C5621 | Approach –Load dataset from source.Split the dataset into “training” and “test” data.Train Decision tree, SVM, and KNN classifiers on the training data.Use the above classifiers to predict labels for the test data.Measure accuracy and visualise classification. | |
C5622 | First multiply the critical value by the standard deviation. Then divide this result by the error from Step 1. Now square this result. This result is the sample size. | |
C5623 | Ordinary linear squares (OLS) regression compares the response of a dependent variable given a change in some explanatory variables. Multiple regressions are based on the assumption that there is a linear relationship between both the dependent and independent variables. | |
C5624 | Rectified linear units, compared to sigmoid function or similar activation functions, allow faster and effective training of deep neural architectures on large and complex datasets. | |
C5625 | Investment risk is the idea that an investment will not perform as expected, that its actual return will deviate from the expected return. Risk is measured by the amount of volatility, that is, the difference between actual returns and average (expected) returns. | |
C5626 | Knowledge is the information about a domain that can be used to solve problems in that domain. As part of designing a program to solve problems, we must define how the knowledge will be represented. A representation scheme is the form of the knowledge that is used in an agent. | |
C5627 | Random binary pattern clustering employing the ART1 net. Different vigilance values cause different numbers of categories (clusters) to form: (a) = 0.5 and (b) = 0.7. For each case, the top row shows prototype vectors extracted by the ART1 network. An example of ART2 clustering is shown in Figure 6.4. | |
C5628 | Data visualization is a technique that uses an array of static and interactive visuals within a specific context to help people understand and make sense of large amounts of data. The data is often displayed in a story format that visualizes patterns, trends and correlations that may otherwise go unnoticed. | |
C5629 | The cross product is a calculation used in order to define the correlation coefficient between two variables. SP is the sum of all cross products between two variables. | |
C5630 | Lemmatization usually refers to doing things properly with the use of a vocabulary and morphological analysis of words, normally aiming to remove inflectional endings only and to return the base or dictionary form of a word, which is known as the lemma . | |
C5631 | While a Machine Learning model makes decisions according to what it has learned from the data, a Neural Network arranges algorithms in a fashion that it can make accurate decisions by itself. Thus, although Machine Learning models can learn from data, in the initial stages, they may require some human intervention. | |
C5632 | Effect of Learning Rate A neural network learns or approximates a function to best map inputs to outputs from examples in the training dataset. A smaller learning rate may allow the model to learn a more optimal or even globally optimal set of weights but may take significantly longer to train. | |
C5633 | The receiver operating characteristic (ROC) curve is a two dimensional graph in which the false positive rate is plotted on the X axis and the true positive rate is plotted on the Y axis. The ROC curves are useful to visualize and compare the performance of classifier methods (see Figure 1). | |
C5634 | The null hypothesis is a general statement that states that there is no relationship between two phenomenons under consideration or that there is no association between two groups. An alternative hypothesis is a statement that describes that there is a relationship between two selected variables in a study. | |
C5635 | In research, there is a convention that the hypothesis is written in two forms, the null hypothesis, and the alternative hypothesis (called the experimental hypothesis when the method of investigation is an experiment). | |
C5636 | Perceptron Learning Rule states that the algorithm would automatically learn the optimal weight coefficients. The input features are then multiplied with these weights to determine if a neuron fires or not. | |
C5637 | 0:395:36Suggested clip · 78 secondsSPSS: Hierarchical Clustering - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5638 | We just need a metric (i.e. loss) to optimize our model. Entropy uses logarithms, computer likes logarithms. We use it. Instead of cross entropy and per-word perplexity of language models lets take a die roll. | |
C5639 | Do not confuse statistical significance with practical importance. However, a weak correlation can be statistically significant, if the sample size is large enough. | |
C5640 | Multi-Armed Bandit Problem This is an Artificial Intelligence (AI) technique in which an agent has to interact with an environment, choosing one of the available actions the environment provides in each possible state, to try and collect as many rewards as possible as a result of those actions. | |
C5641 | For example, the first moment is the expected value E[X]. The second central moment is the variance of X. Similar to mean and variance, other moments give useful information about random variables. The moment generating function (MGF) of a random variable X is a function MX(s) defined as MX(s)=E[esX]. | |
C5642 | 0:083:15Suggested clip · 83 secondsTime Series Forecasting in Minutes - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5643 | The term is often called as corrupt data. We can't avoid the Noise data, but we can reduce it by using noise filters. | |
C5644 | A chi-square is only a nonparametric criterion. You can make comparisons for each characteristic. You can also use Factorial ANOVA. In Factorial ANOVA, you can investigate the dependence of a quantitative characteristic (dependent variable) on one or more qualitative characteristics (category predictors). | |
C5645 | Whereas a variable denotes a placeholder for values taken from a given set, a random variable is the same thing but with the additional datum of a probability measure on the set of values. So, nonrandom variables are precisely those variables which cannot take any values at all. | |
C5646 | Implement them into your life and you'll see results quickly.Spruce up your appearance. Take time for proper grooming and dressing. Set goals and meet them. Confident men make goals and keep them. Exercise. Nothing can boost manly confidence like exercise. Learn a new skill. Take stock of past success. | |
C5647 | Advantages of Naive Bayes ClassifierIt is simple and easy to implement.It doesn't require as much training data.It handles both continuous and discrete data.It is highly scalable with the number of predictors and data points.It is fast and can be used to make real-time predictions.More items• | |
C5648 | Neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training | |
C5649 | Nonparametric tests are also called distribution-free tests because they don't assume that your data follow a specific distribution. You may have heard that you should use nonparametric tests when your data don't meet the assumptions of the parametric test, especially the assumption about normally distributed data. | |
C5650 | Loss is the penalty for a bad prediction. That is, loss is a number indicating how bad the model's prediction was on a single example. If the model's prediction is perfect, the loss is zero; otherwise, the loss is greater. | |
C5651 | Establish face validity.Conduct a pilot test.Enter the pilot test in a spreadsheet.Use principal component analysis (PCA)Check the internal consistency of questions loading onto the same factors.Revise the questionnaire based on information from your PCA and CA. | |
C5652 | 8 Examples of Artificial IntelligenceGoogle Maps and Ride-Hailing Applications. One doesn't have to put much thought into traveling to a new destination anymore. Face Detection and Recognition. Text Editors or Autocorrect. Search and Recommendation Algorithms. Chatbots. Digital Assistants. Social Media. E-Payments. | |
C5653 | The constraints for the maximization problems all involved inequalities, and the constraints for the minimization problems all involved inequalities. Linear programming problems for which the constraints involve both types of inequali- ties are called mixed-constraint problems. | |
C5654 | In order to be considered a normal distribution, a data set (when graphed) must follow a bell-shaped symmetrical curve centered around the mean. It must also adhere to the empirical rule that indicates the percentage of the data set that falls within (plus or minus) 1, 2 and 3 standard deviations of the mean. | |
C5655 | To carry out a Z-test, find a Z-score for your test or study and convert it to a P-value. If your P-value is lower than the significance level, you can conclude that your observation is statistically significant. | |
C5656 | Face validity: Does the content of the test appear to be suitable to its aims? Criterion validity: Do the results correspond to a different test of the same thing? | |
C5657 | Multi-Arm Bandit is a classic reinforcement learning problem, in which a player is facing with k slot machines or bandits, each with a different reward distribution, and the player is trying to maximise his cumulative reward based on trials. | |
C5658 | A sampling error is a statistical error that occurs when an analyst does not select a sample that represents the entire population of data and the results found in the sample do not represent the results that would be obtained from the entire population. | |
C5659 | 1: The number of observations n is fixed. 2: Each observation is independent. 3: Each observation represents one of two outcomes ("success" or "failure"). 4: The probability of "success" p is the same for each outcome. | |
C5660 | Cluster sampling is best used when the clusters occur naturally in a population, when you don't have access to the entire population, and when the clusters are geographically convenient. However, cluster sampling is not as precise as simple random sampling or stratified random sampling. | |
C5661 | In statistics and probability, quantiles are cut points dividing the range of a probability distribution into continuous intervals with equal probabilities, or dividing the observations in a sample in the same way. There is one fewer quantile than the number of groups created. | |
C5662 | 3:2117:13Suggested clip · 116 secondsStepwise regression procedures in SPSS (new, 2018) - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C5663 | In some fields they may be synonyms but in evolutionary computing it can be an important distinction. The objective function is the function being optimised while the fitness function is what is used to guide the optimisation. The fitness function is traditionally positive values with higher being better. | |
C5664 | The formula of population variance is sigma squared equals the sum of x minus the mean squared divided by n. | |
C5665 | Some Final Advantages of Continuous Over Discrete Data The table below lays out the reasons why. Inferences can be made with few data points—valid analysis can be performed with small samples. More data points (a larger sample) needed to make an equivalent inference. Larger samples are usually more expensive to gather. | |
C5666 | In plain words, AIC is a single number score that can be used to determine which of multiple models is most likely to be the best model for a given dataset. It estimates models relatively, meaning that AIC scores are only useful in comparison with other AIC scores for the same dataset. | |
C5667 | In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated. An estimator or decision rule with zero bias is called unbiased. When a biased estimator is used, bounds of the bias are calculated. | |
C5668 | The sample proportion, P is an unbiased estimator of the population proportion, . Unbiased estimators determines the tendency , on the average, for the statistics to assume values closed to the parameter of interest. | |
C5669 | It is usually more straightforward to start from the CDF and then to find the PDF by taking the derivative of the CDF. Note that before differentiating the CDF, we should check that the CDF is continuous. As we will see later, the function of a continuous random variable might be a non-continuous random variable. | |
C5670 | We can interpret the negative binomial regression coefficient as follows: for a one unit change in the predictor variable, the difference in the logs of expected counts of the response variable is expected to change by the respective regression coefficient, given the other predictor variables in the model are held | |
C5671 | There are three types on how batches are defined, one with extremely high scholarships (70–90%), second (10–70%) and third no Scholarships. | |
C5672 | If a variable can take on any value between two specified values, it is called a continuous variable; otherwise, it is called a discrete variable. Some examples will clarify the difference between discrete and continuous variables. The number of heads could be any integer value between 0 and plus infinity. | |
C5673 | The type of quantization in which the quantization levels are uniformly spaced is termed as a Uniform Quantization. The type of quantization in which the quantization levels are unequal and mostly the relation between them is logarithmic, is termed as a Non-uniform Quantization. | |
C5674 | Probability sampling leads to higher quality findings because it provides an unbiased representation of the population. 2. When the population is usually diverse: Researchers use this method extensively as it helps them create samples that fully represent the population. | |
C5675 | Binary classification is one of the most common and frequently tackled problems in the machine learning domain. In it's simplest form the user tries to classify an entity into one of the two possible categories. For example, give the attributes of the fruits like weight, color, peel texture, etc. | |
C5676 | Inferential statistics helps to suggest explanations for a situation or phenomenon. It allows you to draw conclusions based on extrapolations, and is in that way fundamentally different from descriptive statistics that merely summarize the data that has actually been measured. | |
C5677 | The difference is that traditional vision systems involve a human telling a machine what should be there versus a deep learning algorithm automatically extracting the features of what is there. | |
C5678 | Yes. A new computational theory of learning is beginning to shed light on fundamental issues, such as the trade-off among the number of training examples available, the number of hypotheses considered, and the likely accuracy of the learned hypothesis. | |
C5679 | Just so, the Poisson distribution deals with the number of occurrences in a fixed period of time, and the exponential distribution deals with the time between occurrences of successive events as time flows by continuously. | |
C5680 | A weak classifier is simply a classifier that performs poorly, but performs better than random guessing. AdaBoost can be applied to any classification algorithm, so it's really a technique that builds on top of other classifiers as opposed to being a classifier itself. | |
C5681 | weight = weight + learning_rate * (expected - predicted) * x In the Multilayer perceptron, there can more than one linear layer (combinations of neurons). | |
C5682 | Neural networks and fuzzy logic systems are parameterised computational nonlinear algorithms for numerical processing of data (signals, images, stimuli). • These algorithms can be either implemented of a general-purpose computer or built into a dedicated hardware. | |
C5683 | Deep Learning is extensively used for Predictive Analytics, NLP, Computer Vision, and Object Recognition. | |
C5684 | A latent variable is a variable that cannot be observed. The presence of latent variables, however, can be detected by their effects on variables that are observable. Most constructs in research are latent variables. Because measurement error is by definition unique variance, it is not captured in the latent variable. | |
C5685 | The "interquartile range", abbreviated "IQR", is just the width of the box in the box-and-whisker plot. That is, IQR = Q3 – Q1 . The IQR tells how spread out the "middle" values are; it can also be used to tell when some of the other values are "too far" from the central value. | |
C5686 | Feature extraction is a type of dimensionality reduction where a large number of pixels of the image are efficiently represented in such a way that interesting parts of the image are captured effectively. From: Sensors for Health Monitoring, 2019. | |
C5687 | In terms of general theory, random forests can work with both numeric and categorical data. The function randomForest (documentation here) supports categorical data coded as factors, so that would be your type. | |
C5688 | Demeaning data means subtracting the sample mean from each observation so that they are mean zero. Given a simple linear regression Y = alpha + beta X + u, OLS estimation yields Y^ = . | |
C5689 | The Wasserstein loss function seeks to increase the gap between the scores for real and generated images. We can summarize the function as it is described in the paper as follows: Critic Loss = [average critic score on real images] – [average critic score on fake images] | |
C5690 | Alpha sets the standard for how extreme the data must be before we can reject the null hypothesis. The p-value indicates how extreme the data are. If the p-value is greater than alpha (p > . 05), then we fail to reject the null hypothesis, and we say that the result is statistically nonsignificant (n.s.). | |
C5691 | d is used for a perfect differentiation of a function w.r.t a function . delta is used for demonstrating a large and finite change . the partial derivative symbol is used when a multi-variable function is to be differentiated w.r.t only a particular variable , while treating the other variables as constants . | |
C5692 | Variability and Sample Sizes Increasing or decreasing sample sizes leads to changes in the variability of samples. For example, a sample size of 10 people taken from the same population of 1,000 will very likely give you a very different result than a sample size of 100. Next: Sampling Distributions. | |
C5693 | The process of adjusting the weights and threshold of the ADALINE network is based on a learning algorithm named the Delta rule (Widrow and Hoff 1960) or Widrow-Hoff learning rule, also known as LMS (Least Mean Square ) algorithm or Gradient Descent method. | |
C5694 | Just having them in your face each and every day will subconsciously help you learn to recognize them in live trading.Pennant.Cup And Handle.Ascending Triangle.Triple Bottom.Descending Triangle.Inverse Head And Shoulders.Bullish Symmetric Triangle.Rounding Bottom.More items• | |
C5695 | their joint probability distribution at (x,y), the functions given by: g(x) = Σy f (x,y) and h(y) = Σx f (x,y) are the marginal distributions of X and Y , respectively. If you're great with equations, that's probably all you need to know. It tells you how to find a marginal distribution. | |
C5696 | Advertisements. Interpolation search is an improved variant of binary search. This search algorithm works on the probing position of the required value. For this algorithm to work properly, the data collection should be in a sorted form and equally distributed. | |
C5697 | Theoretically, convolution are linear operations on the signal or signal modifiers, whereas correlation is a measure of similarity between two signals. As you rightly mentioned, the basic difference between convolution and correlation is that the convolution process rotates the matrix by 180 degrees. | |
C5698 | How to Deal with MulticollinearityRemove some of the highly correlated independent variables.Linearly combine the independent variables, such as adding them together.Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression. | |
C5699 | Steps for Using ANOVAStep 1: Compute the Variance Between. First, the sum of squares (SS) between is computed: Step 2: Compute the Variance Within. Again, first compute the sum of squares within. Step 3: Compute the Ratio of Variance Between and Variance Within. This is called the F-ratio. |
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