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C7700 | Systematic sampling is frequently used to select a specified number of records from a computer file. Stratified sampling is commonly used probability method that is superior to random sampling because it reduces sampling error. A stratum is a subset of the population that share at least one common characteristic. | |
C7701 | Set the equation equal to zero for each set of parentheses in the fully-factored binomial. For 2x^3 - 16 = 0, for example, the fully factored form is 2(x - 2)(x^2 + 2x + 4) = 0. Set each individual equation equal to zero to get x - 2 = 0 and x^2 + 2x + 4 = 0. Solve each equation to get a solution to the binomial. | |
C7702 | Time series data means that data is in a series of particular time periods or intervals. The data is considered in three types: Time series data: A set of observations on the values that a variable takes at different times. Cross-sectional data: Data of one or more variables, collected at the same point in time. | |
C7703 | Convolutional Neural Networks have a different architecture than regular Neural Networks. Every layer is made up of a set of neurons, where each layer is fully connected to all neurons in the layer before. Finally, there is a last fully-connected layer — the output layer — that represent the predictions. | |
C7704 | Statistics can never "prove" anything. All a statistical test can do is assign a probability to the data you have, indicating the likelihood (or probability) that these numbers come from random fluctuations in sampling. | |
C7705 | Stream processing is the processing of data in motion, or in other words, computing on data directly as it is produced or received. The majority of data are born as continuous streams: sensor events, user activity on a website, financial trades, and so on – all these data are created as a series of events over time. | |
C7706 | Put simply: random forest builds multiple decision trees and merges them together to get a more accurate and stable prediction. Random forest has nearly the same hyperparameters as a decision tree or a bagging classifier. Random forest adds additional randomness to the model, while growing the trees. | |
C7707 | The MSE is a measure of the quality of an estimator—it is always non-negative, and values closer to zero are better. For an unbiased estimator, the MSE is the variance of the estimator. Like the variance, MSE has the same units of measurement as the square of the quantity being estimated. | |
C7708 | The decision of which statistical test to use depends on the research design, the distribution of the data, and the type of variable. In general, if the data is normally distributed, parametric tests should be used. If the data is non-normal, non-parametric tests should be used. | |
C7709 | 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. | |
C7710 | Efficiency: For an unbiased estimator, efficiency indicates how much its precision is lower than the theoretical limit of precision provided by the Cramer-Rao inequality. A measure of efficiency is the ratio of the theoretically minimal variance to the actual variance of the estimator. | |
C7711 | 12 Common Logical Fallacies and How to Debunk Them12 Common Logical Fallacies and How to Debunk Them. Ad Hominem. Appeal to Authority. Bandwagon Argument, or ad populum. The Strawman. Circular Reasoning. The Genetic Fallacy. Anecdotal Evidence.More items• | |
C7712 | TensorFlow provides a collection of workflows to develop and train models using Python, JavaScript, or Swift, and to easily deploy in the cloud, on-prem, in the browser, or on-device no matter what language you use. The tf. data API enables you to build complex input pipelines from simple, reusable pieces. | |
C7713 | Bootstrapping: When you estimate something based on another estimation. In the case of Q-learning for example this is what is happening when you modify your current reward estimation rt by adding the correction term maxa′Q(s′,a′) which is the maximum of the action value over all actions of the next state. | |
C7714 | The purpose of a neural network is to learn to recognize patterns in your data. Once the neural network has been trained on samples of your data, it can make predictions by detecting similar patterns in future data. Software that learns is truly "Artificial Intelligence". | |
C7715 | BFS vs DFS BFS stands for Breadth First Search. DFS stands for Depth First Search. DFS(Depth First Search) uses Stack data structure. 3. BFS can be used to find single source shortest path in an unweighted graph, because in BFS, we reach a vertex with minimum number of edges from a source vertex. | |
C7716 | In terms of the courtroom example, a type I error corresponds to convicting an innocent defendant. Type II error. The second kind of error is the failure to reject a false null hypothesis as the result of a test procedure. | |
C7717 | Logistic regression is quite different than linear regression in that it does not make several of the key assumptions that linear and general linear models (as well as other ordinary least squares algorithm based models) hold so close: (1) logistic regression does not require a linear relationship between the dependent | |
C7718 | In statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values. | |
C7719 | Log likelihood is just the log of the likelihood. The difference between two log likelihoods (on the same data) does have meaning. It is an indicator of how much better one model fits than another, and it is used in a lot of ways which, again, you can read about in books on logistic regression. | |
C7720 | Genetic algorithms usually perform well on discrete data, whereas neural networks usually perform efficiently on continuous data. Genetic algorithms can fetch new patterns, while neural networks use training data to classify a network. Genetic algorithms calculate the fitness function repeatedly to get a good solution. | |
C7721 | The following IIMs are part of CAP 2021:IIM Ranchi – Conducting body of CAP 2021.IIM Trichy.IIM Raipur.IIM Udaipur.IIM Kashipur.IIM Amritsar.IIM Bodh Gaya.IIM Sambalpur. | |
C7722 | Replaces an image by the norm of its gradient, as estimated by discrete filters. The Raw filter of the detail panel designates two filters that correspond to the two components of the gradient in the principal directions. | |
C7723 | Machine learning algorithms are almost always optimized for raw, detailed source data. Thus, the data environment must provision large quantities of raw data for discovery-oriented analytics practices such as data exploration, data mining, statistics, and machine learning. | |
C7724 | Descriptive statistics summarize the characteristics of a data set. Inferential statistics allow you to test a hypothesis or assess whether your data is generalizable to the broader population. | |
C7725 | Here are some strategies that would aid in the effectiveness of your AI deployment:Combine machine learning automation & human evaluation with your data. Marry the data research efforts with project management best practices. Develop a flexible development methodology. Centralize your AI and ML data.More items | |
C7726 | You can use the covariance to determine the direction of a linear relationship between two variables as follows:If both variables tend to increase or decrease together, the coefficient is positive.If one variable tends to increase as the other decreases, the coefficient is negative. | |
C7727 | Properties. Unlike the classical conditional entropy, the conditional quantum entropy can be negative. Positive conditional entropy of a state thus means the state cannot reach even the classical limit, while the negative conditional entropy provides for additional information. | |
C7728 | Often, researchers choose significance levels equal to 0.01, 0.05, or 0.10; but any value between 0 and 1 can be used. Test method. Use the chi-square test for independence to determine whether there is a significant relationship between two categorical variables. | |
C7729 | Linear regression attempts to model the relationship between two variables by fitting a linear equation to observed data. One variable is considered to be an explanatory variable, and the other is considered to be a dependent variable. | |
C7730 | Do you know how to choose the right machine learning algorithm among 7 different types?1-Categorize the problem. 2-Understand Your Data. Analyze the Data. Process the data. Transform the data. 3-Find the available algorithms. 4-Implement machine learning algorithms. 5-Optimize hyperparameters.More items | |
C7731 | The coefficient of variation (CV) is a measure of relative variability. It is the ratio of the standard deviation to the mean (average). For example, the expression “The standard deviation is 15% of the mean” is a CV. | |
C7732 | Binning is the process of combining charge from adjacent pixels in a CCD during readout. The two primary benefits of binning are improved signal-to-noise ratio (SNR) and the ability to increase frame rate, albeit at the expense of reduced spatial resolution. | |
C7733 | Interview Answer Risk means potential threat that calls for identification and careful monitoring of KPI's. | |
C7734 | The Four Probability Rules P(A or B)=P(A)+P(B)−P(A and B) Specifically, if event A is already known to have occurred and probability of event B is desired, then we have the following rule. P(B, given A)=P(A and B)P(A) In set notation, this is written as P(B|A)=P(A∩B)P(A). | |
C7735 | It can work on categorical data and will give you a statistical likelihood of which categorical value (or values) a cluster is most likely to take on. | |
C7736 | Competitive learning is a form of unsupervised learning in artificial neural networks, in which nodes compete for the right to respond to a subset of the input data. Models and algorithms based on the principle of competitive learning include vector quantization and self-organizing maps (Kohonen maps). | |
C7737 | Find a confidence level for a data set by taking half of the size of the confidence interval, multiplying it by the square root of the sample size and then dividing by the sample standard deviation. Look up the resulting Z or t score in a table to find the level. | |
C7738 | 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. | |
C7739 | 5:5217:59Suggested clip · 118 secondsHow to Use SPSS-Hierarchical Multiple Regression - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7740 | 2:324:34Suggested clip · 65 secondsSignal Processing - 24 Convolution - Explained - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7741 | If X takes values in [a, b] and Y takes values in [c, d] then the pair (X, Y ) takes values in the product [a, b] × [c, d]. The joint probability density function (joint pdf) of X and Y is a function f(x, y) giving the probability density at (x, y). | |
C7742 | Fans believe "What if I told you" was said by Morpheus when he was explaining the Matrix to Neo (Keanu Reeves). Per KnowYourMeme, chances are good the "What if I told you" line was merely a reworded take on Morpheus' actual dialogue in the scene: "Do you want to know what 'it' is?". | |
C7743 | Given a character sequence and a defined document unit, tokenization is the task of chopping it up into pieces, called tokens , perhaps at the same time throwing away certain characters, such as punctuation. | |
C7744 | Advantages. The main advantage of multivariate analysis is that since it considers more than one factor of independent variables that influence the variability of dependent variables, the conclusion drawn is more accurate. The conclusions are more realistic and nearer to the real-life situation. | |
C7745 | The bootstrap method is a statistical technique for estimating quantities about a population by averaging estimates from multiple small data samples. Importantly, samples are constructed by drawing observations from a large data sample one at a time and returning them to the data sample after they have been chosen. | |
C7746 | The purpose of machine learning is to discover patterns in your data and then make predictions based on often complex patterns to answer business questions, detect and analyse trends and help solve problems. | |
C7747 | If we assume that there is some variation in our data, we will be able to disregard the possibility that either of these standard deviations is zero. Therefore the sign of the correlation coefficient will be the same as the sign of the slope of the regression line. | |
C7748 | Z-tests are statistical calculations that can be used to compare population means to a sample's. T-tests are calculations used to test a hypothesis, but they are most useful when we need to determine if there is a statistically significant difference between two independent sample groups. | |
C7749 | Examples of Sentiment Analysis For instance, sentiment analysis may be performed on Twitter to determine overall opinion on a particular trending topic. Companies and brands often utilize sentiment analysis to monitor brand reputation across social media platforms or across the web as a whole. | |
C7750 | When there is lack of domain understanding for feature introspection , Deep Learning techniques outshines others as you have to worry less about feature engineering . Deep Learning really shines when it comes to complex problems such as image classification, natural language processing, and speech recognition. | |
C7751 | Conditional probability is probability of a second event given a first event has already occurred. A dependent event is when one event influences the outcome of another event in a probability scenario. | |
C7752 | Face detection algorithms typically start by searching for human eyes -- one of the easiest features to detect. The algorithm might then attempt to detect eyebrows, the mouth, nose, nostrils and the iris. The training improves the algorithms' ability to determine whether there are faces in an image and where they are. | |
C7753 | The conversion of a frequency distribution to a probability distribution is also called an adjusted histogram. This is true for continuous random variables. To convert a frequency distribution to a probability distribution, divide area of the bar or interval of x by the total area of all the Bars. | |
C7754 | Statistics is a very good major in terms of job market and salary scale, it also open doors for many graduate courses, unless you are poor at math ,statistics is worth taking. | |
C7755 | 0:172:45Suggested clip · 110 secondsStats: Complement Rule - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7756 | The purpose of statistical inference is to estimate this sample to sample variation or uncertainty. | |
C7757 | For example, a perfect precision and recall score would result in a perfect F-Measure score:F-Measure = (2 * Precision * Recall) / (Precision + Recall)F-Measure = (2 * 1.0 * 1.0) / (1.0 + 1.0)F-Measure = (2 * 1.0) / 2.0.F-Measure = 1.0. | |
C7758 | The two are different. Stoichiometry looks at balancing equations whereas dimensional analysis is looking at the units particular equations take and allowing you to make a determination of final units (and possibly the correctness of your derivation of units for any equations). | |
C7759 | Neural networks work better at predictive analytics because of the hidden layers. Linear regression models use only input and output nodes to make predictions. Neural network also use the hidden layer to make predictions more accurate. That's because it 'learns' the way a human does. | |
C7760 | 6 Freebies to Help You Increase the Performance of Your Object Detection ModelsVisually Coherent Image Mix-up for Object Detection (+3.55% mAP Boost)Classification Head Label Smoothening (+2.16% mAP Boost)Data Pre-processing (Mixed Results)Training Scheduler Revamping (+1.44% mAP Boost)More items | |
C7761 | 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. | |
C7762 | Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). | |
C7763 | A term document matrix is a way of representing the words in the text as a table (or matrix) of numbers. The rows of the matrix represent the text responses to be analysed, and the columns of the matrix represent the words from the text that are to be used in the analysis. | |
C7764 | The Loss Function is one of the important components of Neural Networks. Loss is nothing but a prediction error of Neural Net. And the method to calculate the loss is called Loss Function. In simple words, the Loss is used to calculate the gradients. And gradients are used to update the weights of the Neural Net. | |
C7765 | Definition. The Vector-Space Model (VSM) for Information Retrieval represents documents and queries as vectors of weights. Each weight is a measure of the importance of an index term in a document or a query, respectively. The documents are then returned by the system by decreasing cosine. | |
C7766 | Early stopping is a method that allows you to specify an arbitrarily large number of training epochs and stop training once the model performance stops improving on the validation dataset. | |
C7767 | The variance is the average of the sum of squares (i.e., the sum of squares divided by the number of observations). The standard deviation is the square root of the variance. | |
C7768 | Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners. Ensemble methods are learning models that achieve performance by combining the opinions of multiple learners. | |
C7769 | LDA (Linear Discriminant Analysis) is used when a linear boundary is required between classifiers and QDA (Quadratic Discriminant Analysis) is used to find a non-linear boundary between classifiers. LDA and QDA work better when the response classes are separable and distribution of X=x for all class is normal. | |
C7770 | A z-score tells you how many standard deviations from the mean your result is. You can use your knowledge of normal distributions (like the 68 95 and 99.7 rule) or the z-table to determine what percentage of the population will fall below or above your result. | |
C7771 | Time is a continuous variable. You could turn age into a discrete variable and then you could count it. For example: A person's age in years. | |
C7772 | Acceptance sampling is a statistical measure used in quality control. It allows a company to determine the quality of a batch of products by selecting a specified number for testing. Acceptance sampling solves these problems by testing a representative sample of the product for defects. | |
C7773 | Today, neural networks are used for solving many business problems such as sales forecasting, customer research, data validation, and risk management. For example, at Statsbot we apply neural networks for time-series predictions, anomaly detection in data, and natural language understanding. | |
C7774 | Artificial intelligence can dramatically improve the efficiencies of our workplaces and can augment the work humans can do. When AI takes over repetitive or dangerous tasks, it frees up the human workforce to do work they are better equipped for—tasks that involve creativity and empathy among others. | |
C7775 | A trimmed mean is stated as a mean trimmed by x%, where x is the sum of the percentage of observations removed from both the upper and lower bounds. | |
C7776 | Each neuron in a layer receives an input from all the neurons present in the previous layer—thus, they're densely connected. In other words, the dense layer is a fully connected layer, meaning all the neurons in a layer are connected to those in the next layer. | |
C7777 | In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. | |
C7778 | In digital image processing and computer vision, image segmentation is the process of partitioning a digital image into multiple segments (sets of pixels, also known as image objects). Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. | |
C7779 | Moments are a set of statistical parameters to measure a distribution. Four moments are commonly used: 1st, Mean: the average. 2d, Variance: Standard deviation is the square root of the variance: an indication of how closely the values are spread about the mean. | |
C7780 | A significant advantage of a decision tree is that it forces the consideration of all possible outcomes of a decision and traces each path to a conclusion. It creates a comprehensive analysis of the consequences along each branch and identifies decision nodes that need further analysis. | |
C7781 | Although both techniques have certain similarities, the difference lies in the fact that classification uses predefined classes in which objects are assigned, while clustering identifies similarities between objects, which it groups according to those characteristics in common and which differentiate them from other | |
C7782 | If the car is behind door 1, Monty will not choose it. He'll open door 2 and show a goat 1/2 of the time. If the car is behind door 2, Monty will always open door 3, as he never reveals the car. If the car is behind door 3, Monty will open door 2 100% of the time. | |
C7783 | Techniques for Handling the Missing DataListwise or case deletion. Pairwise deletion. Mean substitution. Regression imputation. Last observation carried forward. Maximum likelihood. Expectation-Maximization. Multiple imputation.More items• | |
C7784 | All the classes may have the same class size or they may have different classes sizes depending on how you group your data. The class interval is always a whole number. | |
C7785 | This lesson explains how to conduct a chi-square goodness of fit test. The test is applied when you have one categorical variable from a single population. It is used to determine whether sample data are consistent with a hypothesized distribution. | |
C7786 | The macrostructure of sleep has a small but consistent correlation with intelligence, with possible nonlinear effects. Individual differences in intelligence may either cause or be a consequence of individual differences in the macrostructure of sleep, such as timing or duration. | |
C7787 | Tokenization is a common task in Natural Language Processing (NLP). Tokens are the building blocks of Natural Language. Tokenization is a way of separating a piece of text into smaller units called tokens. Here, tokens can be either words, characters, or subwords. | |
C7788 | The standard error of the regression (S), also known as the standard error of the estimate, represents the average distance that the observed values fall from the regression line. Conveniently, it tells you how wrong the regression model is on average using the units of the response variable. | |
C7789 | The covariance between X and Y is defined as Cov(X,Y)=E[(X−EX)(Y−EY)]=E[XY]−(EX)(EY).The covariance has the following properties:Cov(X,X)=Var(X);if X and Y are independent then Cov(X,Y)=0;Cov(X,Y)=Cov(Y,X);Cov(aX,Y)=aCov(X,Y);Cov(X+c,Y)=Cov(X,Y);Cov(X+Y,Z)=Cov(X,Z)+Cov(Y,Z);more generally, | |
C7790 | A qualitative variable, also called a categorical variable, is a variable that isn't numerical. It describes data that fits into categories. For example: Eye colors (variables include: blue, green, brown, hazel). | |
C7791 | While Sensitivity measure is used to determine the proportion of actual positive cases, which got predicted correctly, Specificity measure is used to determine the proportion of actual negative cases, which got predicted correctly. | |
C7792 | The interpretation of the odds ratio depends on whether the predictor is categorical or continuous. Odds ratios that are greater than 1 indicate that the even is more likely to occur as the predictor increases. Odds ratios that are less than 1 indicate that the event is less likely to occur as the predictor increases. | |
C7793 | Train Generative Adversarial Network (GAN)Load Training Data.Define Generator Network.Define Discriminator Network.Define Model Gradients, Loss Functions and Scores.Specify Training Options.Train Model.Generate New Images.More items | |
C7794 | Machine learning is changing the world by transforming all segments including healthcare services, education, transport, food, entertainment, and different assembly line and many more. It will impact lives in almost every aspect, including housing, cars, shopping, food ordering, etc. | |
C7795 | Abstract: The generalized likelihood ratio test (GLRT), which is commonly used in composite hypothesis testing problems, is investigated. Conditions for asymptotic optimality of the GLRT in the Neyman-Pearson sense are studied and discussed. | |
C7796 | A null hypothesis is a type of hypothesis used in statistics that proposes that there is no difference between certain characteristics of a population (or data-generating process). For example, a gambler may be interested in whether a game of chance is fair. | |
C7797 | For a spontaneous reaction, the sign on Delta G must be negative. Gibbs free energy relates enthalpy, entropy and temperature. A spontaneous reaction will always occur when Delta H is negative and Delta S is positive, and a reaction will always be non-spontaneous when Delta H is positive and Delta S is negative. | |
C7798 | Wilks' lambda is a measure of how well each function separates cases into groups. It is equal to the proportion of the total variance in the discriminant scores not explained by differences among the groups. Smaller values of Wilks' lambda indicate greater discriminatory ability of the function. | |
C7799 | The essential benefit achieved by using a rolling hash such as the Rabin fingerprint is that it is possible to compute the hash value of the next substring from the previous one by doing only a constant number of operations, independent of the substrings' lengths. |
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