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C6300 | The F-statistic is the test statistic for F-tests. In general, an F-statistic is a ratio of two quantities that are expected to be roughly equal under the null hypothesis, which produces an F-statistic of approximately 1. In order to reject the null hypothesis that the group means are equal, we need a high F-value. | |
C6301 | Then the marginal pdf's (or pmf's = probability mass functions, if you prefer this terminology for discrete random variables) are defined by fY(y) = P(Y = y) and fX(x) = P(X = x). The joint pdf is, similarly, fX,Y(x,y) = P(X = x and Y = y). | |
C6302 | The intercept (often labeled the constant) is the expected mean value of Y when all X=0. Start with a regression equation with one predictor, X. If X sometimes equals 0, the intercept is simply the expected mean value of Y at that value. | |
C6303 | Repeating patterns often show serial correlation when the level of a variable affects its future level. In finance, this correlation is used by technical analysts to determine how well the past price of a security predicts the future price. Serial correlation is also known as autocorrelation or lagged correlation. | |
C6304 | Firstly, the basic difference between the two is that Market basket analysis is a representation for the whole population (to understand the fact that what products are purchased together as a bunch by the users) whereas the collaborative filtering on the other side restricts itself only to a particular user to | |
C6305 | ANOVA is available for both parametric (score data) and non-parametric (ranking/ordering) data. | |
C6306 | A bell curve is a common type of distribution for a variable, also known as the normal distribution. The term "bell curve" originates from the fact that the graph used to depict a normal distribution consists of a symmetrical bell-shaped curve. | |
C6307 | In the binomial distribution, the number of trials is fixed, and we count the number of "successes". Whereas, in the geometric and negative binomial distributions, the number of "successes" is fixed, and we count the number of trials needed to obtain the desired number of "successes". | |
C6308 | The significance level is the probability of rejecting the null hypothesis when it is true. For example, a significance level of 0.05 indicates a 5% risk of concluding that a difference exists when there is no actual difference. | |
C6309 | The Z Score Formula: One Sample Assuming a normal distribution, your z score would be: z = (x – μ) / σ = (190 – 150) / 25 = 1.6. | |
C6310 | 2:2131:26Suggested clip · 109 secondsContinuous Probability Uniform Distribution Problems - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C6311 | Bagging (Bootstrap Aggregation) is used when our goal is to reduce the variance of a decision tree. Here idea is to create several subsets of data from training sample chosen randomly with replacement. Now, each collection of subset data is used to train their decision trees. | |
C6312 | Standardization isn't required for logistic regression. The main goal of standardizing features is to help convergence of the technique used for optimization. Otherwise, you can run your logistic regression without any standardization treatment on the features. | |
C6313 | Any information that is processed by and sent out from a computer or other electronic device is considered output. An example of output is anything viewed on your computer monitor screen, such as the words you type on your keyboard. | |
C6314 | Lasso regression stands for Least Absolute Shrinkage and Selection Operator. It adds penalty term to the cost function. The difference between ridge and lasso regression is that it tends to make coefficients to absolute zero as compared to Ridge which never sets the value of coefficient to absolute zero. | |
C6315 | AI can efficiently analyze user behaviors, deduce a pattern, and identify all sorts of abnormalities or irregularities in the network. With such data, it's much easier to identify cyber vulnerabilities quickly. | |
C6316 | Natural Language processing is considered a difficult problem in computer science. It's the nature of the human language that makes NLP difficult. While humans can easily master a language, the ambiguity and imprecise characteristics of the natural languages are what make NLP difficult for machines to implement. | |
C6317 | A statistical hypothesis is a hypothesis concerning the parameters or from of the probability distribution for a designated population or populations, or, more generally, of a probabilistic mechanism which is supposed to generate the observations. | |
C6318 | A variance of zero indicates that all of the data values are identical. All non-zero variances are positive. A small variance indicates that the data points tend to be very close to the mean, and to each other. A high variance indicates that the data points are very spread out from the mean, and from one another. | |
C6319 | Simple random sampling uses a table of random numbers or an electronic random number generator to select items for its sample. Meanwhile, systematic sampling involves selecting items from an ordered population using a skip or sampling interval. That means that every "nth" data sample is chosen in a large data set. | |
C6320 | Like all regression analyses, the logistic regression is a predictive analysis. Logistic regression is used to describe data and to explain the relationship between one dependent binary variable and one or more nominal, ordinal, interval or ratio-level independent variables. | |
C6321 | Expectation maximization is applicable whenever the data are missing completely at random or missing at random-but unsuitable when the data are not missing at random. | |
C6322 | In general, focus on these specific tips throughout your interview:Think out loud. Showcasing your communication skills is critical in any phone interview. Ask Questions. If anything is unclear about the problem when you first read it over, ask your interviewer. Start simple, then optimize. If hinted, use them. | |
C6323 | Multivariate ANOVA (MANOVA) extends the capabilities of analysis of variance (ANOVA) by assessing multiple dependent variables simultaneously. ANOVA statistically tests the differences between three or more group means. This statistical procedure tests multiple dependent variables at the same time. | |
C6324 | The Pearson correlation evaluates the linear relationship between two continuous variables. The Spearman correlation coefficient is based on the ranked values for each variable rather than the raw data. Spearman correlation is often used to evaluate relationships involving ordinal variables. | |
C6325 | The 7 Types of Artificial Neural Networks ML Engineers Need to KnowModular Neural Networks.Feedforward Neural Network – Artificial Neuron.Radial basis function Neural Network.Kohonen Self Organizing Neural Network.Recurrent Neural Network(RNN)Convolutional Neural Network.Long / Short Term Memory. | |
C6326 | Convolution has applications that include probability, statistics, computer vision, natural language processing, image and signal processing, engineering, and differential equations. | |
C6327 | Training a neural network involves using an optimization algorithm to find a set of weights to best map inputs to outputs. The problem is hard, not least because the error surface is non-convex and contains local minima, flat spots, and is highly multidimensional. | |
C6328 | Let's look at five approaches that you may use on your machine learning project to compare classifiers.Independent Data Samples. Accept the Problems of 10-fold CV. Use McNemar's Test or 5×2 CV. Use a Nonparametric Paired Test. Use Estimation Statistics Instead. | |
C6329 | Cosine similarity takes the angle between two non-zero vectors and calculates the cosine of that angle, and this value is known as the similarity between the two vectors. This similarity score ranges from 0 to 1, with 0 being the lowest (the least similar) and 1 being the highest (the most similar). | |
C6330 | A learning rate that is too large can cause the model to converge too quickly to a suboptimal solution, whereas a learning rate that is too small can cause the process to get stuck. The learning rate is perhaps the most important hyperparameter. If you have time to tune only one hyperparameter, tune the learning rate. | |
C6331 | Before getting features, various image preprocessing techniques like binarization, thresholding, resizing, normalization etc. are applied on the sampled image. After that, feature extraction techniques are applied to get features that will be useful in classifying and recognition of images. | |
C6332 | Preparing Your Dataset for Machine Learning: 8 Basic Techniques That Make Your Data BetterArticulate the problem early.Establish data collection mechanisms.Format data to make it consistent.Reduce data.Complete data cleaning.Decompose data.Rescale data.Discretize data. | |
C6333 | 8 Methods to Boost the Accuracy of a ModelAdd more data. Having more data is always a good idea. Treat missing and Outlier values. Feature Engineering. Feature Selection. Multiple algorithms. Algorithm Tuning. Ensemble methods. | |
C6334 | The deep convolutional generative adversarial network, or DCGAN for short, is an extension of the GAN architecture for using deep convolutional neural networks for both the generator and discriminator models and configurations for the models and training that result in the stable training of a generator model. | |
C6335 | Deconvolution layer is a very unfortunate name and should rather be called a transposed convolutional layer. Visually, for a transposed convolution with stride one and no padding, we just pad the original input (blue entries) with zeroes (white entries) (Figure 1). | |
C6336 | Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. So predicting a probability of . 012 when the actual observation label is 1 would be bad and result in a high loss value. A perfect model would have a log loss of 0. | |
C6337 | In computer science, a universal Turing machine (UTM) is a Turing machine that simulates an arbitrary Turing machine on arbitrary input. Alan Turing introduced the idea of such a machine in 1936–1937. | |
C6338 | Fixed effects are variables that are constant across individuals; these variables, like age, sex, or ethnicity, don't change or change at a constant rate over time. They have fixed effects; in other words, any change they cause to an individual is the same. | |
C6339 | Two-Tailed Test The rejection region is the region where, if our test statistic falls, then we have enough evidence to reject the null hypothesis. If we consider the right-tailed test, for example, the rejection region is any value greater than c 1 − α , where c 1 − α is the critical value. | |
C6340 | The average score is 100 - that is what the process of standardisation is all about. The top 30% are those that the Bucks system is designed to select, and therefore they will score 121+. | |
C6341 | Loss function for Logistic Regression is the data set containing many labeled examples, which are pairs. is the label in a labeled example. Since this is logistic regression, every value of must either be 0 or 1. is the predicted value (somewhere between 0 and 1), given the set of features in . | |
C6342 | Another common example of univariate analysis is the mean of a population distribution. Tables, charts, polygons, and histograms are all popular methods for displaying univariate analysis of a specific variable (e.g. mean, median, mode, standard variation, range, etc). | |
C6343 | Some Disadvantages of KNNAccuracy depends on the quality of the data.With large data, the prediction stage might be slow.Sensitive to the scale of the data and irrelevant features.Require high memory – need to store all of the training data.Given that it stores all of the training, it can be computationally expensive. | |
C6344 | Example of Stratified Random Sampling Suppose a research team wants to determine the GPA of college students across the U.S. The research team has difficulty collecting data from all 21 million college students; it decides to take a random sample of the population by using 4,000 students. | |
C6345 | Descriptive statistics. The expected value and variance of a Poisson-distributed random variable are both equal to λ. , while the index of dispersion is 1. | |
C6346 | The step that Agglomerative Clustering take are:Each data point is assigned as a single cluster.Determine the distance measurement and calculate the distance matrix.Determine the linkage criteria to merge the clusters.Update the distance matrix.Repeat the process until every data point become one cluster. | |
C6347 | Probability Density Functions are a statistical measure used to gauge the likely outcome of a discrete value, e.g., the price of a stock or ETF. PDFs are plotted on a graph typically resembling a bell curve, with the probability of the outcomes lying below the curve. | |
C6348 | “The difference is that, in the Bayesian approach, the parameters that we are trying to estimate are treated as random variables. In the frequentist approach, they are fixed. Random variables are governed by their parameters (mean, variance, etc.) and distributions (Gaussian, Poisson, binomial, etc). | |
C6349 | (i) The value of dimensionless constants cannot be determined by this method. (ii) This method cannot be applied to equations involving exponential and trigonometric functions. (iii) It cannot be applied to an equation involving more than three physical quantities. | |
C6350 | 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. A simpler formula is: , N is the total Frequency and w is the interval of x. | |
C6351 | In the case of a pair of random variables (X, Y), when random variable X (or Y) is considered by itself, its density function is called the marginal density function. | |
C6352 | Probability is about a finite set of possible outcomes, given a probability. Likelihood is about an infinite set of possible probabilities, given an outcome. | |
C6353 | LBP algorithm | |
C6354 | There are numerous applications of integrals. Using technology such as computer software, internet sources, graphing calculators and smartphone apps can make solving integral problems easier. Some applications of integrals are: Displacement, which is the integral of velocity with respect to time. | |
C6355 | A univariate distribution refers to the distribution of a single random variable. On the other hand, a multivariate distribution refers to the probability distribution of a group of random variables. For example, a multivariate normal distribution is used to specify the probabilities of returns of a group of n stocks. | |
C6356 | K-Fold CV is where a given data set is split into a K number of sections/folds where each fold is used as a testing set at some point. Lets take the scenario of 5-Fold cross validation(K=5). This process is repeated until each fold of the 5 folds have been used as the testing set. | |
C6357 | The adjusted R-squared is a modified version of R-squared that has been adjusted for the number of predictors in the model. The adjusted R-squared increases only if the new term improves the model more than would be expected by chance. It decreases when a predictor improves the model by less than expected by chance. | |
C6358 | A convolution is the simple application of a filter to an input that results in an activation. Repeated application of the same filter to an input results in a map of activations called a feature map, indicating the locations and strength of a detected feature in an input, such as an image. | |
C6359 | A fundamental problem with stepwise regression is that some real explanatory variables that have causal effects on the dependent variable may happen to not be statistically significant, while nuisance variables may be coincidentally significant. | |
C6360 | 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. | |
C6361 | Qualities of a Good Sampling FrameInclude all individuals in the target population.Exclude all individuals not in the target population.Includes accurate information that can be used to contact selected individuals. | |
C6362 | 7:3021:58Suggested clip · 120 secondsStatQuest: Principal Component Analysis (PCA), Step-by-Step YouTubeStart of suggested clipEnd of suggested clip | |
C6363 | Stochastic gradient descent (SGD) computes the gradient for each update using a single training data point x_i (chosen at random). The idea is that the gradient calculated this way is a stochastic approximation to the gradient calculated using the entire training data. | |
C6364 | A two-tailed test will test both if the mean is significantly greater than x and if the mean significantly less than x. The mean is considered significantly different from x if the test statistic is in the top 2.5% or bottom 2.5% of its probability distribution, resulting in a p-value less than 0.05. | |
C6365 | Text generation with an RNNTable of contents.Setup. Import TensorFlow and other libraries. Download the Shakespeare dataset. Process the text. Vectorize the text. The prediction task. Build The Model.Try the model.Train the model. Attach an optimizer, and a loss function. Generate text. Restore the latest checkpoint. Advanced: Customized Training. | |
C6366 | The maximum entropy principle is defined as modeling a given set of data by finding the highest entropy to satisfy the constraints of our prior knowledge. The maximum entropy model is a conditional probability model p(y|x) that allows us to predict class labels given a set of features for a given data point. | |
C6367 | 4:1410:53Suggested clip · 113 secondsStochastic Gradient Descent, Clearly Explained!!! - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C6368 | Boolean searching allows the user to combine or limit words and phrases in an online search in order to retrieve relevant results. Using the Boolean terms: AND, OR, NOT, the searcher is able to define relationships among concepts. Use OR to broaden search results. | |
C6369 | 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. | |
C6370 | How to Avoid Confirmation Bias. Look for ways to challenge what you think you see. Seek out information from a range of sources, and use an approach such as the Six Thinking Hats technique to consider situations from multiple perspectives. Alternatively, discuss your thoughts with others. | |
C6371 | Statistical stationarity: A stationary time series is one whose statistical properties such as mean, variance, autocorrelation, etc. are all constant over time. Such statistics are useful as descriptors of future behavior only if the series is stationary. | |
C6372 | Factor analysis is a statistical method used to describe variability among observed, correlated variables in terms of a potentially lower number of unobserved variables called factors. Factor analysis is related to principal component analysis (PCA), but the two are not identical. | |
C6373 | Sampling error is affected by a number of factors including sample size, sample design, the sampling fraction and the variability within the population. | |
C6374 | Multinomial Naïve Bayes uses term frequency i.e. the number of times a given term appears in a document. After normalization, term frequency can be used to compute maximum likelihood estimates based on the training data to estimate the conditional probability. | |
C6375 | metric system. A system of measurement in which the basic units are the meter, the second, and the kilogram. In this system, the ratios between units of measurement are multiples of ten. For example, a kilogram is a thousand grams, and a centimeter is one-hundredth of a meter. | |
C6376 | In statistics and probability analysis, the expected value is calculated by multiplying each of the possible outcomes by the likelihood each outcome will occur and then summing all of those values. By calculating expected values, investors can choose the scenario most likely to give the desired outcome. | |
C6377 | In machine learning, multiclass or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification). | |
C6378 | The marketplace for predictive analytics software has ballooned: G2Crowd records 92 results in the category. Pricing varies substantially based on the number of users and, in some cases, amount of data, but generally starts around $1,000 per year, though it can easily scale into six figures. | |
C6379 | When error terms from different (usually adjacent) time periods (or cross-section observations) are correlated, we say that the error term is serially correlated. Serial correlation occurs in time-series studies when the errors associated with a given time period carry over into future time periods. | |
C6380 | Without Further Ado, The Top 10 Machine Learning Algorithms for Beginners:Linear Regression. In machine learning, we have a set of input variables (x) that are used to determine an output variable (y). Logistic Regression. CART. Naïve Bayes. KNN. | |
C6381 | Our picks for the best statistics and probability courses for data scientists are…Foundations of Data Analysis — Part 1: Statistics Using R by the University of Texas at Austin (edX)Foundations of Data Analysis — Part 2: Inferential Statistics by the University of Texas at Austin (edX) | |
C6382 | SVD gives you the whole nine-yard of diagonalizing a matrix into special matrices that are easy to manipulate and to analyze. It lay down the foundation to untangle data into independent components. PCA skips less significant components. | |
C6383 | An environment is everything in the world which surrounds the agent, but it is not a part of an agent itself. An environment can be described as a situation in which an agent is present. The environment is where agent lives, operate and provide the agent with something to sense and act upon it. | |
C6384 | Mean, variance, and standard deviation The mean of the sampling distribution of the sample mean will always be the same as the mean of the original non-normal distribution. In other words, the sample mean is equal to the population mean. | |
C6385 | As a general rule, GPUs are a safer bet for fast machine learning because, at its heart, data science model training is composed of simple matrix math calculations, the speed of which can be greatly enhanced if the computations can be carried out in parallel. | |
C6386 | Response bias can be defined as the difference between the true values of variables in a study's net sample group and the values of variables obtained in the results of the same study. Nonresponse bias occurs when some respondents included in the sample do not respond. | |
C6387 | Probability Role of probability in statistics: Use probability to predict results of experiment under assumptions. Compute probability of error larger than given amount. Compute probability of given departure between prediction and results under assumption. | |
C6388 | In statistics, a central tendency (or measure of central tendency) is a central or typical value for a probability distribution. It may also be called a center or location of the distribution. The most common measures of central tendency are the arithmetic mean, the median, and the mode. | |
C6389 | A layer is a callable object that takes as input one or more tensors and that outputs one or more tensors. It involves computation, defined in the call() method, and a state (weight variables), defined either in the constructor __init__() or in the build() method. | |
C6390 | 0:001:55Suggested clip · 86 secondsLogistic Functions - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C6391 | This is calculated as the outer product between the actual rating's histogram vector of ratings and the predicted rating's histogram vector of ratings, normalized such that E and O have the same sum. From these three matrices, the quadratic weighted kappa is calculated. | |
C6392 | A SIFT descriptor is a 3-D spatial histogram of the image gradients in characterizing the appearance of a keypoint. The gradient at each pixel is regarded as a sample of a three-dimensional elementary feature vector, formed by the pixel location and the gradient orientation. | |
C6393 | In a simple case with two possible categories or the binary classification problem you have one boundary. Negative means you want the output to be off/low when the classifier “sees” that particular class. Positive means you want the output to be on/high when the classifier “sees” that class. | |
C6394 | Noun. 1. XY - (genetics) normal complement of sex hormones in a male. sex chromosome - (genetics) a chromosome that determines the sex of an individual; "mammals normally have two sex chromosomes" | |
C6395 | Recall is the number of relevant documents retrieved by a search divided by the total number of existing relevant documents, while precision is the number of relevant documents retrieved by a search divided by the total number of documents retrieved by that search. | |
C6396 | 9:3918:24Suggested clip · 119 secondsR - Regression Trees - CART - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C6397 | Static Rules Approach. The most simple, and maybe the best approach to start with, is using static rules. The Idea is to identify a list of known anomalies and then write rules to detect those anomalies. Rules identification is done by a domain expert, by using pattern mining techniques, or a by combination of both. | |
C6398 | Cross-sectional data, or a cross section of a study population, in statistics and econometrics is a type of data collected by observing many subjects (such as individuals, firms, countries, or regions) at the one point or period of time. The analysis might also have no regard to differences in time. | |
C6399 | One of the most common assumptions in many machine learning and data analysis tasks is that the given data points are realizations of independent and identically distributed (IID) random variables. |
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