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C1700 | You can use regression equations to make predictions. Regression equations are a crucial part of the statistical output after you fit a model. However, you can also enter values for the independent variables into the equation to predict the mean value of the dependent variable. | |
C1701 | The Mean Squared Error (MSE) is a measure of how close a fitted line is to data points. The MSE has the units squared of whatever is plotted on the vertical axis. Another quantity that we calculate is the Root Mean Squared Error (RMSE). It is just the square root of the mean square error. | |
C1702 | The dependent variable is the variable being tested and measured in an experiment, and is 'dependent' on the independent variable. An example of a dependent variable is depression symptoms, which depends on the independent variable (type of therapy). | |
C1703 | In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method proposed by Thomas Cover used for classification and regression. In both cases, the input consists of the k closest training examples in the feature space. | |
C1704 | In edge detection, we find the boundaries or edges of objects in an image, by determining where the brightness of the image changes dramatically. Edge detection can be used to extract the structure of objects in an image. | |
C1705 | 12 Common Biases That Affect How We Make Everyday DecisionsThe Dunning-Kruger Effect. Confirmation Bias. Self-Serving Bias. The Curse of Knowledge and Hindsight Bias. Optimism/Pessimism Bias. The Sunk Cost Fallacy. Negativity Bias. The Decline Bias (a.k.a. Declinism)More items• | |
C1706 | Attention-based models belong to a class of models commonly called sequence-to-sequence models. The aim of these models, as name suggests, it to produce an output sequence given an input sequence which are, in general, of different lengths. | |
C1707 | Semi-Markov decision processes (SMDPs), generalize MDPs by allowing the state transitions to occur in continuous irregular times. In this framework, after the agent takes action a in state s, the environment will remain in state s for time d and then transits to the next state and the agent receives the reward r. | |
C1708 | Downsampling an image When data is removed the image also degrades to some extent, although not nearly as much as when you upsample. By removing this extra data ( downsampling) this results in a much smaller file size. For example, you can see below that our original image was 17.2 MB at 3000 by 2000 pixels. | |
C1709 | In statistical hypothesis testing, a type I error is the rejection of a true null hypothesis (also known as a "false positive" finding or conclusion; example: "an innocent person is convicted"), while a type II error is the non-rejection of a false null hypothesis (also known as a "false negative" finding or conclusion | |
C1710 | In cluster sampling, researchers divide a population into smaller groups known as clusters.You thus decide to use the cluster sampling method.Step 1: Define your population. Step 2: Divide your sample into clusters. Step 3: Randomly select clusters to use as your sample. Step 4: Collect data from the sample. | |
C1711 | The shape of the t distribution changes with sample size. As the sample size increases the t distribution becomes more and more like a standard normal distribution. In fact, when the sample size is infinite, the two distributions (t and z) are identical. | |
C1712 | Standard deviation tells you how spread out the data is. It is a measure of how far each observed value is from the mean. In any distribution, about 95% of values will be within 2 standard deviations of the mean. | |
C1713 | The mean for the standard normal distribution is zero, and the standard deviation is one. The transformation z=x−μσ z = x − μ σ produces the distribution Z ~ N(0, 1). | |
C1714 | Automatic thresholdingSelect initial threshold value, typically the mean 8-bit value of the original image.Divide the original image into two portions; Find the average mean values of the two new images.Calculate the new threshold by averaging the two means.More items | |
C1715 | categorization have not been convincingly shown. In this work we demonstrated that image segmentation can in fact improve object recognition and categorization and it also adds object localization and multi-class categorization ca- pabilities to an off-the-shelf categorization system. | |
C1716 | Coreference resolution is the task of finding all expressions that refer to the same entity in a text. It is an important step for a lot of higher level NLP tasks that involve natural language understanding such as document summarization, question answering, and information extraction. | |
C1717 | Digital image processing, as a computer-based technology, carries out automatic processing, manipulation and interpretation of such visual information, and it plays an increasingly important role in many aspects of our daily life, as well as in a wide variety of disciplines and fields in science and technology, with | |
C1718 | The three main metrics used to evaluate a classification model are accuracy, precision, and recall. Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily by dividing the number of correct predictions by the number of total predictions. | |
C1719 | 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. | |
C1720 | An HMM topology is defined as the statistical behavior of an observable symbol sequence in terms of a network of states, which represents the overall process behavior with regard to movement between states of the process, and describes the inherent variations in the behavior of the observable symbols within a state. | |
C1721 | In statistical classification, Bayes error rate is the lowest possible error rate for any classifier of a random outcome (into, for example, one of two categories) and is analogous to the irreducible error. A number of approaches to the estimation of the Bayes error rate exist. | |
C1722 | The Poisson parameter Lambda (λ) is the total number of events (k) divided by the number of units (n) in the data (λ = k/n). | |
C1723 | Dense layer is the regular deeply connected neural network layer. It is most common and frequently used layer. Dense layer does the below operation on the input and return the output. dot represent numpy dot product of all input and its corresponding weights. | |
C1724 | The eigenvalues and eigenvectors of a matrix are often used in the analysis of financial data and are integral in extracting useful information from the raw data. They can be used for predicting stock prices and analyzing correlations between various stocks, corresponding to different companies. | |
C1725 | Random forest does handle missing data and there are two distinct ways it does so: 1) Without imputation of missing data, but providing inference. 2) Imputing the data. Prior to splitting a node, missing data for a variable is imputed by randomly drawing values from non-missing in-bag data. | |
C1726 | In supervised learning applications in machine learning and statistical learning theory, generalization error (also known as the out-of-sample error) is a measure of how accurately an algorithm is able to predict outcome values for previously unseen data. | |
C1727 | The coefficient of determination is the square of the correlation (r) between predicted y scores and actual y scores; thus, it ranges from 0 to 1. With linear regression, the coefficient of determination is also equal to the square of the correlation between x and y scores. | |
C1728 | The independent variable is called the Explanatory variable (or better known as the predictor) - the variable which influences or predicts the values. i.e. if the explanatory variable changes then it affects the response variable. Here Y is the Dependent variable or response variable. | |
C1729 | TL;DR: It is possible to learn Data Science with Low-Code experience. There are some basic principles of data science that you need to learn before learning Python, and you can start solving many real world problems without any coding at all! | |
C1730 | Multiclass classification with logistic regression can be done either through the one-vs-rest scheme in which for each class a binary classification problem of data belonging or not to that class is done, or changing the loss function to cross- entropy loss. | |
C1731 | The technological singularity—also, simply, the singularity—is a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization. The first use of the concept of a "singularity" in the technological context was John von Neumann. | |
C1732 | In cryptography, padding is any of a number of distinct practices which all include adding data to the beginning, middle, or end of a message prior to encryption. | |
C1733 | A Latent Class regression model: Is used to predict a dependent variable as a function of predictor variables (Regression model). Includes a K-category latent variable X to cluster cases (LC model) Each case may contain multiple records (Regression with repeated measurements). | |
C1734 | A CNN has multiple layers. Weight sharing happens across the receptive field of the neurons(filters) in a particular layer. Weights are the numbers within each filter. These filters act on a certain receptive field/ small section of the image. When the filter moves through the image, the filter does not change. | |
C1735 | The main difference between the two, is that a Perceptron takes that binary response (like a classification result) and computes an error used to update the weights, whereas an Adaline uses a continous response value to update the weights (so before the binarized output is produced). | |
C1736 | Bennett University. White noise is used in context of linear regression. It refers to a case when residuals (errors) are random and come from a single N(0, sigma^2) distribution. Clearly, the residuals are iid with a condition that their expectation is zero. | |
C1737 | Feature extraction is a general term for methods of constructing combinations of the variables to get around these problems while still describing the data with sufficient accuracy. Many machine learning practitioners believe that properly optimized feature extraction is the key to effective model construction. | |
C1738 | Hidden Markov model (HMM) has been successfully used for sequential data modeling problems. In the proposed GenHMM, each HMM hidden state is associated with a neural network based generative model that has tractability of exact likelihood and provides efficient likelihood computation. | |
C1739 | The standard deviation (SD) measures the amount of variability, or dispersion, from the individual data values to the mean, while the standard error of the mean (SEM) measures how far the sample mean of the data is likely to be from the true population mean. SD is the dispersion of individual data values. | |
C1740 | How to Choose a Machine Learning Model – Some GuidelinesCollect data.Check for anomalies, missing data and clean the data.Perform statistical analysis and initial visualization.Build models.Check the accuracy.Present the results. | |
C1741 | Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. | |
C1742 | How to Run Your First Classifier in WekaDownload Weka and Install. Visit the Weka Download page and locate a version of Weka suitable for your computer (Windows, Mac, or Linux). Start Weka. Start Weka. Open the data/iris. arff Dataset. Select and Run an Algorithm. Review Results. | |
C1743 | In machine learning, the delta rule is a gradient descent learning rule for updating the weights of the inputs to artificial neurons in a single-layer neural network. It is a special case of the more general backpropagation algorithm. # | |
C1744 | How to optimize your meta tags: A checklistCheck whether all your pages and your content have title tags and meta descriptions.Start paying more attention to your headings and how you structure your content.Don't forget to mark up your images with alt text.More items• | |
C1745 | Principal Component Analysis PCA's approach to data reduction is to create one or more index variables from a larger set of measured variables. It does this using a linear combination (basically a weighted average) of a set of variables. The created index variables are called components. | |
C1746 | In the modern context, computational intelligence tends to use bio-inspired computing, like evolutionary and genetic algorithms. AI tends to prefer techniques with stronger theoretical guarantees, and still has a significant community focused on purely deductive reasoning. | |
C1747 | Inter-Rater or Inter-Observer Reliability: Used to assess the degree to which different raters/observers give consistent estimates of the same phenomenon. Test-Retest Reliability: Used to assess the consistency of a measure from one time to another. | |
C1748 | We call vectorization the general process of turning a collection of text documents into numerical feature vectors. Documents are described by word occurrences while completely ignoring the relative position information of the words in the document. | |
C1749 | 2:0210:15Suggested clip · 117 secondsConducting a Multiple Regression using Microsoft Excel Data YouTubeStart of suggested clipEnd of suggested clip | |
C1750 | KNN represents a supervised classification algorithm that will give new data points accordingly to the k number or the closest data points, while k-means clustering is an unsupervised clustering algorithm that gathers and groups data into k number of clusters. | |
C1751 | Classification accuracy is our starting point. It is the number of correct predictions made divided by the total number of predictions made, multiplied by 100 to turn it into a percentage. | |
C1752 | Model calibration is the process of adjustment of the model parameters and forcing within the margins of the uncertainties (in model parameters and / or model forcing) to obtain a model representation of the processes of interest that satisfies pre-agreed criteria (Goodness-of-Fit or Cost Function). | |
C1753 | Boosting is a general ensemble method that creates a strong classifier from a number of weak classifiers. This is done by building a model from the training data, then creating a second model that attempts to correct the errors from the first model. | |
C1754 | The midrange is a type of average, or mean. Electronic gadgets are sometimes classified as “midrange”, meaning they're in the middle-price bracket. The formula to find the midrange = (high + low) / 2. | |
C1755 | There are six broad steps to data wrangling, which are:Discovering. In this step, the data is to be understood more deeply. Structuring. Raw data is given to you in a haphazard manner, in most cases – there will not be any structure to it. Cleaning. Enriching. Validating. Publishing. | |
C1756 | A hidden unit corresponds to the output of a single filter at a single particular x/y offset in the input volume. | |
C1757 | Effect size is a simple way of quantifying the difference between two groups that has many advantages over the use of tests of statistical significance alone. Effect size emphasises the size of the difference rather than confounding this with sample size. A number of alternative measures of effect size are described. | |
C1758 | When the sample size is sufficiently large, the shape of the sampling distribution approximates a normal curve (regardless of the shape of the parent population)! The distribution of sample means is a more normal distribution than a distribution of scores, even if the underlying population is not normal. | |
C1759 | Using Logarithmic Functions Much of the power of logarithms is their usefulness in solving exponential equations. Some examples of this include sound (decibel measures), earthquakes (Richter scale), the brightness of stars, and chemistry (pH balance, a measure of acidity and alkalinity). | |
C1760 | Machine learning and Data Science are intricately linked To take your career as high as you can't even imagine, you can become competent in both these fields, which will enable you to analyse a frightening amount of data, and then proceed to extract value and provide insight on the data. | |
C1761 | Definition. The cumulative distribution function (CDF) of random variable X is defined as FX(x)=P(X≤x), for all x∈R. Note that the subscript X indicates that this is the CDF of the random variable X. Also, note that the CDF is defined for all x∈R. Let us look at an example. | |
C1762 | Accuracy (Figure 1) is a measure of how close an achieved position is to a desired target position. Repeatability (Figure 2) is a measure of a system's consistency to achieve identical results across multiple tests. The ultimate goal is to have both a highly accurate and highly repeatable system. | |
C1763 | Since this impulse response in infinitely long, recursive filters are often called infinite impulse response (IIR) filters. In effect, recursive filters convolve the input signal with a very long filter kernel, although only a few coefficients are involved. | |
C1764 | Definition and Notation In handwriting, a tilde, arrow or underline is used to denote a vector. The convention for handwritten notation varies with geography and subject area. Vectors can be described using Cartesian coordinates, giving the components of the vector along each of the axes. Example: a=(a1,a2,a3). | |
C1765 | Sensitivity is the proportion of patients with disease who test positive. In probability notation: P(T+|D+) = TP / (TP+FN). Specificity is the proportion of patients without disease who test negative. In probability notation: P(T-|D-) = TN / (TN + FP). It is the proportion of total patients who have the disease. | |
C1766 | One of the simplest and yet most important models in time series forecasting is the random walk model. This model assumes that in each period the variable takes a random step away from its previous value, and the steps are independently and identically distributed in size (“i.i.d.”). | |
C1767 | When the order doesn't matter, it is a Combination. When the order does matter it is a Permutation. | |
C1768 | Univariate analysis is the simplest form of analyzing data. “Uni” means “one”, so in other words your data has only one variable. It doesn't deal with causes or relationships (unlike regression ) and it's major purpose is to describe; It takes data, summarizes that data and finds patterns in the data. | |
C1769 | A posterior probability, in Bayesian statistics, is the revised or updated probability of an event occurring after taking into consideration new information. In statistical terms, the posterior probability is the probability of event A occurring given that event B has occurred. | |
C1770 | Hierarchical clustering is a powerful technique that allows you to build tree structures from data similarities. You can now see how different sub-clusters relate to each other, and how far apart data points are. | |
C1771 | Statement: A continuous time signal can be represented in its samples and can be recovered back when sampling frequency fs is greater than or equal to the twice the highest frequency component of message signal. | |
C1772 | It is often used as a gauge of economic inequality, measuring income distribution or, less commonly, wealth distribution among a population. The coefficient ranges from 0 (or 0%) to 1 (or 100%), with 0 representing perfect equality and 1 representing perfect inequality. | |
C1773 | A Markov process is a random process in which the future is independent of the past, given the present. Thus, Markov processes are the natural stochastic analogs of the deterministic processes described by differential and difference equations. They form one of the most important classes of random processes. | |
C1774 | To write a null hypothesis, first start by asking a question. Rephrase that question in a form that assumes no relationship between the variables. In other words, assume a treatment has no effect. Write your hypothesis in a way that reflects this. | |
C1775 | A non-parametric test is a hypothesis test that does not make any assumptions about the distribution of the samples. It does not rely on any properties of the distributions. The null hypothesis is that the samples were drawn from the same distribution. | |
C1776 | Enter (Regression) . A procedure for variable selection in which all variables in a block are entered in a single step. Stepwise . At each step, the independent variable not in the equation that has the smallest probability of F is entered, if that probability is sufficiently small. | |
C1777 | This is because a two-tailed test uses both the positive and negative tails of the distribution. In other words, it tests for the possibility of positive or negative differences. A one-tailed test is appropriate if you only want to determine if there is a difference between groups in a specific direction. | |
C1778 | By Jim Frost 45 Comments. Heteroscedasticity means unequal scatter. In regression analysis, we talk about heteroscedasticity in the context of the residuals or error term. Specifically, heteroscedasticity is a systematic change in the spread of the residuals over the range of measured values. | |
C1779 | As much as I understand, in value iteration, you use the Bellman equation to solve for the optimal policy, whereas, in policy iteration, you randomly select a policy π, and find the reward of that policy. | |
C1780 | There are three primary assumptions in ANOVA: The responses for each factor level have a normal population distribution. These distributions have the same variance. The data are independent. | |
C1781 | An unbiased estimator is an accurate statistic that's used to approximate a population parameter. That's just saying if the estimator (i.e. the sample mean) equals the parameter (i.e. the population mean), then it's an unbiased estimator. | |
C1782 | Grid search is an approach to hyperparameter tuning that will methodically build and evaluate a model for each combination of algorithm parameters specified in a grid. In grid searching, you first define the range of values for each of the hyperparameters a1, a2 and a3. | |
C1783 | In machine learning, a hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are derived via training. Given these hyperparameters, the training algorithm learns the parameters from the data. | |
C1784 | Advantages and Disadvantages of Machine Learning LanguageEasily identifies trends and patterns. Machine Learning can review large volumes of data and discover specific trends and patterns that would not be apparent to humans. No human intervention needed (automation) Continuous Improvement. Handling multi-dimensional and multi-variety data. Wide Applications. | |
C1785 | The least squares principle states that the SRF should be constructed (with the constant and slope values) so that the sum of the squared distance between the observed values of your dependent variable and the values estimated from your SRF is minimized (the smallest possible value). | |
C1786 | A cantilever beam is given an initial deflection and then released. Its vibration is an eigenvalue problem and the eigenvalues are the natural frequencies of vibration and the eigenvectors are the mode shapes of the vibration. | |
C1787 | The cosine similarity is the cosine of the angle between two vectors. Figure 1 shows three 3-dimensional vectors and the angles between each pair. In text analysis, each vector can represent a document. The greater the value of θ, the less the value of cos θ, thus the less the similarity between two documents. | |
C1788 | The stress state is a second order tensor since it is a quantity associated with two directions. As a result, stress components have 2 subscripts. A surface traction is a first order tensor (i.e. vector) since it a quantity associated with only one direction. Vector components therefore require only 1 subscript. | |
C1789 | Traditional programming is a manual process—meaning a person (programmer) creates the program. But without anyone programming the logic, one has to manually formulate or code rules. In machine learning, on the other hand, the algorithm automatically formulates the rules from the data. | |
C1790 | An autoregressive model is when a value from a time series is regressed on previous values from that same time series. In this regression model, the response variable in the previous time period has become the predictor and the errors have our usual assumptions about errors in a simple linear regression model. | |
C1791 | Number of discriminant functions. There is one discriminant function for 2- group discriminant analysis, but for higher order DA, the number of functions is the lesser of (g - 1), where g is the number of groups, or p,the number of discriminating (independent) variables. | |
C1792 | Since the theory is about eigenvalues of linear operators, and Heisenberg and other physicists related the spectral lines seen with prisms or gratings to eigenvalues of certain linear operators in quantum mechanics, it seems logical to explain the name as inspired by relevance of the theory in atomic physics. | |
C1793 | The area percentage (proportion, probability) calculated using a z-score will be a decimal value between 0 and 1, and will appear in a Z-Score Table. The total area under any normal curve is 1 (or 100%). | |
C1794 | An F-test is any statistical test in which the test statistic has an F-distribution under the null hypothesis. It is most often used when comparing statistical models that have been fitted to a data set, in order to identify the model that best fits the population from which the data were sampled. | |
C1795 | A distribution with a single mode is said to be unimodal. A distribution with more than one mode is said to be bimodal, trimodal, etc., or in general, multimodal. | |
C1796 | In statistics, a unimodal probability distribution or unimodal distribution is a probability distribution which has a single peak. The term "mode" in this context refers to any peak of the distribution, not just to the strict definition of mode which is usual in statistics. | |
C1797 | Test statistic. The test statistic is a z-score (z) defined by the following equation. where P is the hypothesized value of population proportion in the null hypothesis, p is the sample proportion, and σ is the standard deviation of the sampling distribution. | |
C1798 | It's greedy because you always mark the closest vertex. It's dynamic because distances are updated using previously calculated values. I would say it's definitely closer to dynamic programming than to a greedy algorithm. To find the shortest distance from A to B, it does not decide which way to go step by step. | |
C1799 | Strongly Connected Components1) Create an empty stack 'S' and do DFS traversal of a graph. In DFS traversal, after calling recursive DFS for adjacent vertices of a vertex, push the vertex to stack. 2) Reverse directions of all arcs to obtain the transpose graph.3) One by one pop a vertex from S while S is not empty. Let the popped vertex be 'v'. |
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