_id stringlengths 2 6 | text stringlengths 3 395 | title stringclasses 1 value |
|---|---|---|
C1400 | A beta weight is a standardized regression coefficient (the slope of a line in a regression equation). A beta weight will equal the correlation coefficient when there is a single predictor variable. β can be larger than +1 or smaller than -1 if there are multiple predictor variables and multicollinearity is present. | |
C1401 | A multivariate normal distribution is a vector in multiple normally distributed variables, such that any linear combination of the variables is also normally distributed. | |
C1402 | Deep Neural Networks (DNN) have greater capabilities for image pattern recognition and are widely used in Computer Vision algorithms. And, Convolutional Neural Network (CNN, or ConvNet) is a class of DNN which is most commonly applied to analyzing visual imagery. | |
C1403 | Spatial mining is the extraction of knowledge/spatial relationship and interesting measures that are not explicitly stored in spatial database. Temporal mining is the extraction of knowledge about occurrence of an event whether they follow Cyclic , Random ,Seasonal variations etc. | |
C1404 | where 'In' denotes the n-by-n identity matrix. The matrix B is called the inverse matrix of A. A square matrix is Invertible if and only if its determinant is non-zero. | |
C1405 | Statistically significant means a result is unlikely due to chance. The p-value is the probability of obtaining the difference we saw from a sample (or a larger one) if there really isn't a difference for all users. | |
C1406 | In information theory, the information content, self-information, surprisal, or Shannon information is a basic quantity derived from the probability of a particular event occurring from a random variable. The Shannon information can be interpreted as quantifying the level of "surprise" of a particular outcome. | |
C1407 | 5 years | |
C1408 | Affecting Entropy If you increase temperature, you increase entropy. (1) More energy put into a system excites the molecules and the amount of random activity. (2) As a gas expands in a system, entropy increases. | |
C1409 | There is a wide rangeof statistical tests. There are many different types of tests in statistics like t-test,Z-test,chi-square test, anova test ,binomial test, one sample median test etc. Choosing a Statistical test- Parametric tests are used if the data is normally distributed . | |
C1410 | LSTM ( Long Short Term Memory ) Networks are called fancy recurrent neural networks with some additional features. Rolled Network. Just like RNN, we have time steps in LSTM but we have extra piece of information which is called “MEMORY” in LSTM cell for every time step. | |
C1411 | Sparse Approximation (also known as Sparse Representation) theory deals with sparse solutions for systems of linear equations. Techniques for finding these solutions and exploiting them in applications have found wide use in image processing, signal processing, machine learning, medical imaging, and more. | |
C1412 | Unsupervised learning is the Holy Grail of Deep Learning. The goal of unsupervised learning is to create general systems that can be trained with little data. Today Deep Learning models are trained on large supervised datasets. Meaning that for each data, there is a corresponding label. | |
C1413 | Reinforcement learning (RL) is a machine learning technique that focuses on training an algorithm following the cut-and-try approach. The algorithm (agent) evaluates a current situation (state), takes an action, and receives feedback (reward) from the environment after each act. | |
C1414 | In econometrics, the seemingly unrelated regressions (SUR) or seemingly unrelated regression equations (SURE) model, proposed by Arnold Zellner in (1962), is a generalization of a linear regression model that consists of several regression equations, each having its own dependent variable and potentially different sets | |
C1415 | Matrix Inventory allows you to add and manage product lists that consist of similar items that are available in a variety of attributes, such as size or color. Each product is defined by a combination of attributes is a unique product with its own price, inventory and/or recipe. | |
C1416 | While in Gradient Descent (GD) the whole Training Set is considered before taking one Model Parameters Update Step, in Stochastic Gradient Descent (SGD) only one Data Point is considered for each Model Parameters Update Step, cycling over the Training Set. | |
C1417 | Analysis of covariance is used to test the main and interaction effects of categorical variables on a continuous dependent variable, controlling for the effects of selected other continuous variables, which co-vary with the dependent. The control variables are called the "covariates." | |
C1418 | Disadvantage:A small change in the data can cause a large change in the structure of the decision tree causing instability.For a Decision tree sometimes calculation can go far more complex compared to other algorithms.Decision tree often involves higher time to train the model.More items | |
C1419 | Statistics is a mathematically-based field which seeks to collect and interpret quantitative data. In contrast, data science is a multidisciplinary field which uses scientific methods, processes, and systems to extract knowledge from data in a range of forms. | |
C1420 | Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could apply when trying to recognize trucks. | |
C1421 | 7 Techniques to Handle Imbalanced DataUse the right evaluation metrics. Resample the training set. Use K-fold Cross-Validation in the right way. Ensemble different resampled datasets. Resample with different ratios. Cluster the abundant class. Design your own models. | |
C1422 | Dimensional Analysis (also called Factor-Label Method or the Unit Factor Method) is a problem-solving method that uses the fact that any number or expression can be multiplied by one without changing its value. It is a useful technique. | |
C1423 | f(x) = Pr[X = x] The following is the plot of the normal probability density function. Cumulative Distribution Function. The cumulative distribution function (cdf) is the probability that the variable takes a value less than or equal to x. | |
C1424 | Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. | |
C1425 | Feature Selection: Select a subset of input features from the dataset.Unsupervised: Do not use the target variable (e.g. remove redundant variables). Correlation.Supervised: Use the target variable (e.g. remove irrelevant variables). Wrapper: Search for well-performing subsets of features. RFE. | |
C1426 | Two events are independent, statistically independent, or stochastically independent if the occurrence of one does not affect the probability of occurrence of the other (equivalently, does not affect the odds). | |
C1427 | The framework, which stands for Setting, People, Alternatives, Decide and Explain, has been used to make important calls, without depending on the slow crawl of consensus decision-making. | |
C1428 | In statistics, the one in ten rule is a rule of thumb for how many predictor parameters can be estimated from data when doing regression analysis (in particular proportional hazards models in survival analysis and logistic regression) while keeping the risk of overfitting low. | |
C1429 | Ensemble model combines multiple 'individual' (diverse) models together and delivers superior prediction power. If you want to relate this to real life, a group of people are likely to make better decisions compared to individuals, especially when group members come from diverse background. | |
C1430 | A curve or pattern in the residual plot indicates a nonlinear relationship in the original data set. A random scatter of points in the residual plot indicates a linear relationship in the original data set. | |
C1431 | Marginal probability: the probability of an event occurring (p(A)), it may be thought of as an unconditional probability. It is not conditioned on another event. Example: the probability that a card drawn is red (p(red) = 0.5). | |
C1432 | US, informal. 1 or hash over : to talk about (something) : discuss (something) The detectives hashed out their theories about who committed the murder. They've spent quite a bit of time hashing over the problem. | |
C1433 | The input nodes take in information, in the form which can be numerically expressed. The information is presented as activation values, where each node is given a number, the higher the number, the greater the activation. The output nodes then reflect the input in a meaningful way to the outside world. | |
C1434 | Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. Its goal is to maximize the total reward. | |
C1435 | Linear Discriminant Analysis or Normal Discriminant Analysis or Discriminant Function Analysis is a dimensionality reduction technique which is commonly used for the supervised classification problems. It is used for modeling differences in groups i.e. separating two or more classes. | |
C1436 | Autocorrelation, also known as serial correlation, is the correlation of a signal with a delayed copy of itself as a function of delay. It is often used in signal processing for analyzing functions or series of values, such as time domain signals. | |
C1437 | Feature weighting is a technique used to approximate the optimal degree of influence of individual features using a training set. When successfully applied relevant features are attributed a high weight value, whereas irrelevant features are given a weight value close to zero. | |
C1438 | A distribution is skewed if one of its tails is longer than the other. The first distribution shown has a positive skew. This means that it has a long tail in the positive direction. The distribution below it has a negative skew since it has a long tail in the negative direction. | |
C1439 | 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. | |
C1440 | The median is the number in the middle {2, 3, 11, 13, 26, 34, 47}, which in this instance is 13 since there are three numbers on either side. To find the median value in a list with an even amount of numbers, one must determine the middle pair, add them, and divide by two. | |
C1441 | ReLU stands for rectified linear unit, and is a type of activation function. Mathematically, it is defined as y = max(0, x). Visually, it looks like the following: ReLU is the most commonly used activation function in neural networks, especially in CNNs. | |
C1442 | A One-tailed Test Is Used When The Null Hypothesis Should Be Rejected If The Test Value Is In The Critical Region On One Side Of The Mean. A Two-tailed Test Is Used When The Null Hypothesis Should Be Rejected If The Test Value Is In The Critical Region On Either | |
C1443 | The previous module introduced the idea of dividing your data set into two subsets: training set—a subset to train a model. test set—a subset to test the trained model. | |
C1444 | Word embeddings are distributed representations of text in an n-dimensional space. These are essential for solving most NLP problems. Word vectors are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space.” | |
C1445 | ANSWER. A false positive means that the results say you have the condition you were tested for, but you really don't. With a false negative, the results say you don't have a condition, but you really do. | |
C1446 | A/B tests are easy and seem harmless, but many consumers become disturbed when they find out they're being tested without knowing it. Some argue that A/B testing tracks along the same ethical lines as a product launch; others believe organizations must be transparent about their testing even if it seems harmless. | |
C1447 | The function of kernel is to take data as input and transform it into the required form. Different SVM algorithms use different types of kernel functions. These functions can be different types. For example linear, nonlinear, polynomial, radial basis function (RBF), and sigmoid. | |
C1448 | 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. | |
C1449 | The Fourier transform of a function of time is a complex-valued function of frequency, whose magnitude (absolute value) represents the amount of that frequency present in the original function, and whose argument is the phase offset of the basic sinusoid in that frequency. | |
C1450 | According to a senior data scientist, one of the distinct advantages of using Stochastic Gradient Descent is that it does the calculations faster than gradient descent and batch gradient descent. However, gradient descent is the best approach if one wants a speedier result. | |
C1451 | The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. | |
C1452 | A histogram is drawn like a bar chart, but often has bars of unequal width. It is the area of the bar that tells us the frequency in a histogram, not its height. Instead of plotting frequency on the y-axis, we plot the frequency density. To calculate this, you divide the frequency of a group by the width of it. | |
C1453 | A discrete variable is a variable whose value is obtained by counting. A continuous variable is a variable whose value is obtained by measuring. A discrete random variable X has a countable number of possible values. Example: Let X represent the sum of two dice. | |
C1454 | An activation function is defined by and defines the output of a neuron in terms of its input (aka induced local field) . There are three types of activation functions. Threshhold function an example of which is. This function is also termed the Heaviside function. Piecewise Linear. | |
C1455 | In addition, scales can be constructed from categorical variables. This is covered in a later section. The Count property returns the number of levels in the scale. The IsOrdered property indicates whether the scale is ordered or unordered. | |
C1456 | Ordered probit, like ordered logit, is a particular method of ordinal regression. The ordered probit model provides an appropriate fit to these data, preserving the ordering of response options while making no assumptions of the interval distances between options. | |
C1457 | Five main Component of Natural Language processing are:Morphological and Lexical Analysis.Syntactic Analysis.Semantic Analysis.Discourse Integration.Pragmatic Analysis. | |
C1458 | Some of the most popular machine learning algorithms for creating text classification models include the naive bayes family of algorithms, support vector machines, and deep learning.Naive Bayes. Support Vector Machines. Deep Learning. Text Classification with R. | |
C1459 | How to train your Deep Neural NetworkTraining data. Choose appropriate activation functions. Number of Hidden Units and Layers. Weight Initialization. Learning Rates. Hyperparameter Tuning: Shun Grid Search - Embrace Random Search. Learning Methods. Keep dimensions of weights in the exponential power of 2.More items• | |
C1460 | The hazard ratio is a clinical trial statistic that allows the physician to say with confidence that healing is faster with the new drug. The hazard ratio must be >1 and the lower limit of the 95% confidence interval of the hazard ratio must be >1, which was the case in this example. | |
C1461 | The Real World is a term by the redpills to refer to reality, the true physical world and life outside the Matrix. | |
C1462 | The use of sigmoidal nonlinear functions was inspired by the ouputs of biological neurons. However, this function is not smooth (it fails to be differential at the threshold value). Therefore, the sigmoid class of functions is a differentiable alternative that still captures much of the behavior of biological neurons. | |
C1463 | The Chi-Square Test for Normality allows us to check whether or not a model or theory follows an approximately normal distribution. The Chi-Square Test for Normality is not as powerful as other more specific tests (like Lilliefors). | |
C1464 | Explanation: There are total three types of questions that can be put to a regression analysis, that are, causal analysis, forecasting and affect and trend forecasting. | |
C1465 | Values must be positive as log(x) exists only for positive values of x. The shape of the lognormal distribution is defined by three parameters: σ, the shape parameter. Also the standard deviation for the lognormal, this affects the general shape of the distribution. | |
C1466 | The Monty Hall problem is one of those rare curiosities – a mathematical problem that has made the front pages of national news. Everyone now knows, or thinks they know, the answer but a realistic look at the problem demonstrates that the standard mathematician's answer is wrong. | |
C1467 | Find all of your absolute errors, xi – x. Add them all up. Divide by the number of errors. For example, if you had 10 measurements, divide by 10.Mean Absolute Errorn = the number of errors,Σ = summation symbol (which means “add them all up”),|xi – x| = the absolute errors. | |
C1468 | Variational autoencoders (VAEs) are a deep learning technique for learning latent representations. They have also been used to draw images, achieve state-of-the-art results in semi-supervised learning, as well as interpolate between sentences. There are many online tutorials on VAEs. | |
C1469 | For example for a t-test, we assume that a random variable follows a normal distribution. For discrete data key distributions are: Bernoulli, Binomial, Poisson and Multinomial. | |
C1470 | 3 Answers. Attempts to find an average value of AC would directly provide you the answer zero Hence, RMS values are used. They help to find the effective value of AC (voltage or current). This RMS is a mathematical quantity (used in many math fields) used to compare both alternating and direct currents (or voltage). | |
C1471 | For example, polynomial regression consists of performing multiple regression with variables. in order to find the polynomial coefficients (parameters). These types of regression are known as parametric regression since they are based on models that require the estimation of a finite number of parameters. | |
C1472 | Model fitting is a measure of how well a machine learning model generalizes to similar data to that on which it was trained. During the fitting process, you run an algorithm on data for which you know the target variable, known as “labeled” data, and produce a machine learning model. | |
C1473 | Association rule learning is a rule-based machine learning method for discovering interesting relations between variables in large databases. It is intended to identify strong rules discovered in databases using some measures of interestingness. | |
C1474 | Template matching is a technique for finding areas of an image that match (or are similar) to a template image which requires two images. Source image (I): The image in which we expect to find a match to the template image. Template image (T): The patch image which will be compared to the template image. | |
C1475 | Neural networks: A mathematical model used to predict and classify results from the given data set is referred to as neural networks. They contain a set of algorithms and functions similar to that of a neuron of the brain. A neural network classifies the inputs by the process of learning. | |
C1476 | The component form of simple exponential smoothing is given by: Forecast equation^yt+h|t=ℓtSmoothing equationℓt=αyt+(1−α)ℓt−1, Forecast equation y ^ t + h | t = ℓ t Smoothing equation ℓ t = α y t + ( 1 − α ) ℓ t − 1 , where ℓt is the level (or the smoothed value) of the series at time t . | |
C1477 | 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. | |
C1478 | The general formula for calculating a harmonic mean is:Harmonic mean = n / (∑1/x_i)Weighted Harmonic Mean = (∑w_i ) / (∑w_i/x_i)P/E (Index) = (0.4+0.6) / (0.4/50 + 0.6/4) = 6.33.P/E (Index) = 0.4×50 + 0.6×4 = 22.4. | |
C1479 | Parameters are like exogenous variables in that their values are taken as given. They are distinct, however, from exogenous variables in that they tend to represent things that are given by nature such as consumer preferences or production technologies. | |
C1480 | The main difference between the t-test and f-test is, that t-test is used to test the hypothesis whether the given mean is significantly different from the sample mean or not. On the other hand, an F-test is used to compare the two standard deviations of two samples and check the variability. | |
C1481 | A continuous random variable takes a range of values, which may be finite or infinite in extent. Here are a few examples of ranges: [0, 1], [0, ∞), (−∞, ∞), [a, b]. The function f(x) is called the probability density function (pdf). | |
C1482 | A graphical representation of a single dataset, tallied into classes. The graph consists of a series of rectangles whose widths are defined by the limits of the classes, and whose heights are calculated by dividing relative frequency by class width. | |
C1483 | N-grams are contiguous sequences of n-items in a sentence. N can be 1, 2 or any other positive integers, although usually we do not consider very large N because those n-grams rarely appears in many different places. This post describes several different ways to generate n-grams quickly from input sentences in Python. | |
C1484 | Alternative procedures include: Different linear model: fitting a linear model with additional X variable(s) Nonlinear model: fitting a nonlinear model when the linear model is inappropriate. Weighted least squares linear regression: dealing with unequal variances in Y by performing a weighted least squares fit. | |
C1485 | The main advantage of quantile regression methodology is that the method allows for understanding relationships between variables outside of the mean of the data,making it useful in understanding outcomes that are non-normally distributed and that have nonlinear relationships with predictor variables. | |
C1486 | To increase the stability of a neural network, batch normalization normalizes the output of a previous activation layer by subtracting the batch mean and dividing by the batch standard deviation. | |
C1487 | Basically CV<10 is very good, 10-20 is good, 20-30 is acceptable, and CV>30 is not acceptable. | |
C1488 | Gans can not be directly applied for natural language as the space in which sentence are present is not continuous and thereby not differentiable. | |
C1489 | We calibrate our model when the probability estimate of a data point belonging to a class is very important. Calibration is comparison of the actual output and the expected output given by a system. | |
C1490 | KNN algorithm is one of the simplest classification algorithm and it is one of the most used learning algorithms. KNN is a non-parametric, lazy learning algorithm. Its purpose is to use a database in which the data points are separated into several classes to predict the classification of a new sample point. | |
C1491 | An estimator of a given parameter is said to be unbiased if its expected value is equal to the true value of the parameter. In other words, an estimator is unbiased if it produces parameter estimates that are on average correct. | |
C1492 | The standard deviation of this set of mean values is the standard error. In lieu of taking many samples one can estimate the standard error from a single sample. This estimate is derived by dividing the standard deviation by the square root of the sample size. | |
C1493 | There are four assumptions associated with a linear regression model: Linearity: The relationship between X and the mean of Y is linear. Homoscedasticity: The variance of residual is the same for any value of X. Independence: Observations are independent of each other. | |
C1494 | “Expert systems” basically set a number of “if this, then do that” statements. It does not learn by itself (so it is not machine learning), and it still can be very useful for use cases like medical diagnosis and treatment. | |
C1495 | Batch size controls the accuracy of the estimate of the error gradient when training neural networks. Batch, Stochastic, and Minibatch gradient descent are the three main flavors of the learning algorithm. There is a tension between batch size and the speed and stability of the learning process. | |
C1496 | Collinearity is a linear association between two predictors. Multicollinearity is a situation where two or more predictors are highly linearly related. But, correlation 'among the predictors' is a problem to be rectified to be able to come up with a reliable model. | |
C1497 | Word2vec is a technique for natural language processing. The word2vec algorithm uses a neural network model to learn word associations from a large corpus of text. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. | |
C1498 | (definition) Definition: A computational problem in which the object is to find the best of all possible solutions. More formally, find a solution in the feasible region which has the minimum (or maximum) value of the objective function. | |
C1499 | While many people use the terms interchangeably, data science and big data analytics are unique fields, with the major difference being the scope. Data science produces broader insights that concentrate on which questions should be asked, while big data analytics emphasizes discovering answers to questions being asked. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.