_id stringlengths 2 6 | text stringlengths 3 395 | title stringclasses 1 value |
|---|---|---|
C10400 | If the absolute value of the t-value is greater than the critical value, you reject the null hypothesis. If the absolute value of the t-value is less than the critical value, you fail to reject the null hypothesis. | |
C10401 | Statistical modeling is the process of applying statistical analysis to a dataset. A statistical model is a mathematical representation (or mathematical model) of observed data. | |
C10402 | A feedforward network with a single layer is sufficient to represent any function, but the layer may be infeasibly large and may fail to learn and generalize correctly. If you accept most classes of problems can be reduced to functions, this statement implies a neural network can, in theory, solve any problem. | |
C10403 | Weighted grade calculation The weighted grade is equal to the sum of the product of the weights (w) in percent (%) times the grade (g): Weighted grade = w1×g1+ w2×g2+ w3×g3+ | |
C10404 | The mean is the most common measure of center. It is what most people think of when they hear the word "average". However, the mean is affected by extreme values so it may not be the best measure of center to use in a skewed distribution. The median is the value in the center of the data. | |
C10405 | Despite the sample population being selected in advance, systematic sampling is still thought of as being random if the periodic interval is determined beforehand and the starting point is random. | |
C10406 | Definition. Imitation is the ability to recognize and reproduce others' actions – By extension, imitation learning is a means of learning and developing new skills from observing these skills performed by another agent. | |
C10407 | A t-value is the relative error difference in contrast to the null hypothesis. A p-value, is the statistical significance of a measurement in how correct a statistical evidence part, is. | |
C10408 | A one-tailed test is also known as a directional hypothesis or directional test. A two-tailed test, on the other hand, is designed to examine both sides of a specified data range to test whether a sample is greater than or less than the range of values. | |
C10409 | ·4 min read N-gram is probably the easiest concept to understand in the whole machine learning space, I guess. An N-gram means a sequence of N words. So for example, “Medium blog” is a 2-gram (a bigram), “A Medium blog post” is a 4-gram, and “Write on Medium” is a 3-gram (trigram). | |
C10410 | Proportional-integral-derivative (PID) controllers that can automatically select their own tuning parameters sound good but face challenges. The exercise is conceptually simple: Choose the gain, rate, and reset parameters that define the relative magnitude of the P, I, and D contributions to the overall control effort. | |
C10411 | The Monty Hall problem has confused people for decades. In the game show, Let's Make a Deal, Monty Hall asks you to guess which closed door a prize is behind. The answer is so puzzling that people often refuse to accept it! The problem occurs because our statistical assumptions are incorrect. | |
C10412 | The basic idea behind a neural network is to simulate (copy in a simplified but reasonably faithful way) lots of densely interconnected brain cells inside a computer so you can get it to learn things, recognize patterns, and make decisions in a humanlike way. | |
C10413 | It is used when we want to predict the value of a variable based on the value of two or more other variables. For example, you could use multiple regression to understand whether exam performance can be predicted based on revision time, test anxiety, lecture attendance and gender. | |
C10414 | The Bayesian framework for machine learning states that you start out by enumerating all reasonable models of the data and assigning your prior belief P(M) to each of these models. Then, upon observing the data D, you evaluate how probable the data was under each of these models to compute P(D|M). | |
C10415 | A regression coefficient is the same thing as the slope of the line of the regression equation. The equation for the regression coefficient that you'll find on the AP Statistics test is: B1 = b1 = Σ [ (xi – x)(yi – y) ] / Σ [ (xi – x)2]. “y” in this equation is the mean of y and “x” is the mean of x. | |
C10416 | There are several differences between these two frameworks. Keras is a neural network library while TensorFlow is the open-source library for a number of various tasks in machine learning. TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs. | |
C10417 | The frame refers to the list of units (eg, persons, households, businesses, etc) in the survey population. It determines how well a target population is covered, and affects the choice of the data collection method. | |
C10418 | Answer. A negative path loading is basically the same as a negative regression coefficient. I.e., For a path loading from X to Y it is the predicted increase in Y for a one unit increase on X holding all other variables constant. So a negative coefficient just means that as X increases, Y is predicted to decrease. | |
C10419 | You can use tf. function to make graphs out of your programs. It is a transformation tool that creates Python-independent dataflow graphs out of your Python code. This will help you create performant and portable models, and it is required to use SavedModel . function works under the hood so you can use it effectively. | |
C10420 | When you perform a t-test, you're usually trying to find evidence of a significant difference between population means (2-sample t) or between the population mean and a hypothesized value (1-sample t). The t-value measures the size of the difference relative to the variation in your sample data. | |
C10421 | Multivariate interpolation is the interpolation of functions of more than one variable. Methods include bilinear interpolation and bicubic interpolation in two dimensions, and trilinear interpolation in three dimensions. They can be applied to gridded or scattered data. | |
C10422 | The least squares approach limits the distance between a function and the data points that the function explains. It is used in regression analysis, often in nonlinear regression modeling in which a curve is fit into a set of data. Mathematicians use the least squares method to arrive at a maximum-likelihood estimate. | |
C10423 | KNN for Classification And the inverse, use an even number for K when you have an odd number of classes. Ties can be broken consistently by expanding K by 1 and looking at the class of the next most similar instance in the training dataset. | |
C10424 | 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, | |
C10425 | If you reduce the random error of a data set, you reduce the width (FULL WIDTH AT HALF MAXIMUM) of a distribution, or the counting noise (POISSON NOISE) of a measurement. Usually, you can reduce random error by simply taking more measurements. | |
C10426 | var·i·ance ra·ti·o (F), the distribution of the ratio of two independent estimates of the same variance from a gaussian distribution based on samples of sizes (n + 1) and (m + 1), respectively. | |
C10427 | 8 Radial Basis Function Networks. Radial basis function (RBF) networks are a commonly used type of artificial neural network for function approximation problems. An RBF network is a type of feed forward neural network composed of three layers, namely the input layer, the hidden layer and the output layer. | |
C10428 | Clustering is the task of dividing the population or data points into a number of groups such that data points in the same groups are more similar to other data points in the same group than those in other groups. In simple words, the aim is to segregate groups with similar traits and assign them into clusters. | |
C10429 | The expected number of false positives if the rate is set at 5% should be 5%. In general, this rate is higher, because investigators fail to include all sources of uncertainty when calculating the expected false positive rate. | |
C10430 | Word vectors are simply vectors of numbers that represent the meaning of a word. In simpler terms, a word vector is a row of real-valued numbers (as opposed to dummy numbers) where each point captures a dimension of the word's meaning and where semantically similar words have similar vectors. | |
C10431 | In programming languages In Fortran, R, APL, J and Wolfram Language (Mathematica), it is done through simple multiplication operator * , whereas the matrix product is done through the function matmul , %*% , +. | |
C10432 | To find the interquartile range (IQR), first find the median (middle value) of the lower and upper half of the data. These values are quartile 1 (Q1) and quartile 3 (Q3). The IQR is the difference between Q3 and Q1. | |
C10433 | Differentiation and integration can help us solve many types of real-world problems. We use the derivative to determine the maximum and minimum values of particular functions (e.g. cost, strength, amount of material used in a building, profit, loss, etc.). | |
C10434 | The variance of a set of numbers is the mean squared deviation from the mean. It is a measure of how spread out the set of numbers is. The estimation variance is the variance of that large set of values. It measures how much, well, variance there is in an estimator from sample to sample. | |
C10435 | A knowledge-based system (KBS) is a form of artificial intelligence (AI) that aims to capture the knowledge of human experts to support decision-making. Examples of knowledge-based systems include expert systems, which are so called because of their reliance on human expertise. | |
C10436 | Decision Tree - Classification. Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. Decision trees can handle both categorical and numerical data | |
C10437 | In simpler terms, optimizers shape and mold your model into its most accurate possible form by futzing with the weights. The loss function is the guide to the terrain, telling the optimizer when it's moving in the right or wrong direction. | |
C10438 | The difference between combinations and permutations is ordering. With permutations we care about the order of the elements, whereas with combinations we don't. For example, say your locker “combo” is 5432. If you enter 4325 into your locker it won't open because it is a different ordering (aka permutation). | |
C10439 | Neural Turing Machines can take input and output and learn algorithms that map from one to the other. This means that once they have learned that algorithm, they can take a given input, and they can extrapolate based on that algorithm to any variable output. | |
C10440 | Quantization, in mathematics and digital signal processing, is the process of mapping input values from a large set (often a continuous set) to output values in a (countable) smaller set, often with a finite number of elements. Rounding and truncation are typical examples of quantization processes. | |
C10441 | Multimodal means having or using a variety of modes or methods to do something. Multimodal is a general term that can be used in many different contexts. It also has more specific uses in the fields of statistics and transportation. | |
C10442 | General Properties of Probability Distributions The sum of all probabilities for all possible values must equal 1. Furthermore, the probability for a particular value or range of values must be between 0 and 1. Probability distributions describe the dispersion of the values of a random variable. | |
C10443 | Batch normalization (also known as batch norm) is a method used to make artificial neural networks faster and more stable through normalization of the input layer by re-centering and re-scaling. | |
C10444 | In other words, discriminative models are used to specify outputs based on inputs (by models such as Logistic regression, Neural networks and Random forests), while generative models generate both inputs and outputs (for example, by Hidden Markov model, Bayesian Networks and Gaussian mixture model). | |
C10445 | Odds Ratio is a measure of the strength of association with an exposure and an outcome.OR > 1 means greater odds of association with the exposure and outcome.OR = 1 means there is no association between exposure and outcome.OR < 1 means there is a lower odds of association between the exposure and outcome. | |
C10446 | Best! Logistic regression chooses the class that has the biggest probability. In case of 2 classes, the threshold is 0.5: if P(Y=0) > 0.5 then obviously P(Y=0) > P(Y=1). The same stands for the multiclass setting: again, it chooses the class with the biggest probability (see e.g. Ng's lectures, the bottom lines). | |
C10447 | Weighted percentages allow you to account for this. All you have to do is convert the percentage the assignment is worth into a decimal and multiply that by your grade. To convert, just divide the percentage of your final grade the assignment represents by 100. | |
C10448 | To convert a logit ( glm output) to probability, follow these 3 steps:Take glm output coefficient (logit)compute e-function on the logit using exp() “de-logarithimize” (you'll get odds then)convert odds to probability using this formula prob = odds / (1 + odds) . | |
C10449 | EAD, along with loss given default (LGD) and the probability of default (PD), are used to calculate the credit risk capital of financial institutions. Banks often calculate an EAD value for each loan and then use these figures to determine their overall default risk. | |
C10450 | Neural networks are widely used in unsupervised learning in order to learn better representations of the input data. This process doesn't give you clusters, but it creates meaningful representations that can be used for clustering. You could, for instance, run a clustering algorithm on the hidden layer's activations. | |
C10451 | Balance between discriminator & generator We can improve GAN by turning our attention in balancing the loss between the generator and the discriminator. Unfortunately, the solution seems elusive. We can maintain a static ratio between the number of gradient descent iterations on the discriminator and the generator. | |
C10452 | Feature selection methods are intended to reduce the number of input variables to those that are believed to be most useful to a model in order to predict the target variable. Feature selection is primarily focused on removing non-informative or redundant predictors from the model. | |
C10453 | Prior probability, in Bayesian statistical inference, is the probability of an event before new data is collected. | |
C10454 | It can be human- or machine-generated. Examples of unstructured data include: Media: Audio and video files, images. Text files: Word docs, PowerPoint presentations, email, chat logs. | |
C10455 | Categorical variables require special attention in regression analysis because, unlike dichotomous or continuous variables, they cannot by entered into the regression equation just as they are. Instead, they need to be recoded into a series of variables which can then be entered into the regression model. | |
C10456 | There is a popular method known as elbow method which is used to determine the optimal value of K to perform the K-Means Clustering Algorithm. The basic idea behind this method is that it plots the various values of cost with changing k. As the value of K increases, there will be fewer elements in the cluster. | |
C10457 | So, if we want to say how widely scattered some measurements are, we use the standard deviation. If we want to indicate the uncertainty around the estimate of the mean measurement, we quote the standard error of the mean. The standard error is most useful as a means of calculating a confidence interval. | |
C10458 | 2:316:15Suggested clip · 118 secondsUnit Conversion the Easy Way (Dimensional Analysis) - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C10459 | 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 | |
C10460 | Continuous variables can take on an unlimited number of values between the lowest and highest points of measurement. Continuous variables include such things as speed and distance. Discrete data are associated with a limited number of possible values. | |
C10461 | Monte Carlo tree search algorithm | |
C10462 | The most significant difference between regression vs classification is that while regression helps predict a continuous quantity, classification predicts discrete class labels. There are also some overlaps between the two types of machine learning algorithms. | |
C10463 | The median divides the data into a lower half and an upper half. The lower quartile is the middle value of the lower half. The upper quartile is the middle value of the upper half. The following figure shows the median, quartiles and interquartile range. | |
C10464 | Content validity: Is the test fully representative of what it aims to measure? Criterion validity: Do the results correspond to a different test of the same thing? | |
C10465 | The lower class boundary is found by subtracting 0.5 units from the lower class limit and the upper class boundary is found by adding 0.5 units to the upper class limit. The difference between the upper and lower boundaries of any class. | |
C10466 | Econometrics originally came from statistics. In general statistics is more general than econometrics, since while econometrics focuses in Statistical Inference, Statistics also deals with other important fields such as Design of Experiments and Sampling techiniques. | |
C10467 | Vector autoregression (VAR) is a statistical model used to capture the relationship between multiple quantities as they change over time. VAR models generalize the single-variable (univariate) autoregressive model by allowing for multivariate time series. VAR models are often used in economics and the natural sciences. | |
C10468 | -A field is a variable that exists inside of an object, while a parameter is a variable inside a method whose value is passed in from outside. | |
C10469 | Shift-invariance: this means that if we shift the input in time (or shift the entries in a vector) then the output is shifted by the same amount. Mathematically, we can say that if f(x(t)) = y(t), shift invariance means that f(x(t + ⌧)) = y(t + ⌧). | |
C10470 | Arrange data points from smallest to largest and locate the central number. This is the median. If there are 2 numbers in the middle, the median is the average of those 2 numbers. The mode is the number in a data set that occurs most frequently. | |
C10471 | Under the batch processing model, a set of data is collected over time, then fed into an analytics system. In other words, you collect a batch of information, then send it in for processing. Under the streaming model, data is fed into analytics tools piece-by-piece. The processing is usually done in real time. | |
C10472 | While regular gradient boosting uses the loss function of our base model (e.g. decision tree) as a proxy for minimizing the error of the overall model, XGBoost uses the 2nd order derivative as an approximation. | |
C10473 | 0:315:15Suggested clip · 110 secondsMultinomial Distributions: Examples (Basic Probability and Statistics YouTubeStart of suggested clipEnd of suggested clip | |
C10474 | Security groups are tied to an instance whereas Network ACLs are tied to the subnet. i.e. Network Access control lists are applicable at the subnet level, so any instance in the subnet with an associated NACL will follow rules of NACL. This means any instances within the subnet group gets the rule applied. | |
C10475 | If you have severely imbalanced classes, you can get high overall accuracy without much effort — but without generating any good insights. The overall accuracy might be high, but for the minority class, you will have very low recall. | |
C10476 | Logits are values that are used as input to softmax. To understand this better click here this is official by tensorflow. Therefore, +ive logits correspond to probability of greater than 0.5 and negative corresponds to a probability value of less than 0.5. Sometimes they are also refer to inverse of sigmoid function. | |
C10477 | There are two types of hierarchical clustering, Divisive and Agglomerative. | |
C10478 | There are two main differences between regression and structural equation modelling. The first is that SEM allows us to develop complex path models with direct and indirect effects. This allows us to more accurately model causal mechanisms we are interested in. The second key difference is to do with measurement. | |
C10479 | Variable screening is the process of filtering out irrelevant variables, with the aim to reduce the dimensionality from ultrahigh to high while retaining all important variables. The main theme of this thesis is to develop variable screening and variable selection methods for high dimensional data analysis. | |
C10480 | The mean ^ n . of these values is the expected value of the estimator :^. (3+2+5+3+6+5)/6 = 24/6 = 4. Thus, the expected value of the estimator is 4; this is denoted as E( ). | |
C10481 | XOR function | |
C10482 | Select a random sample.Create 2 or more groups by manipulating the levels of an IV.Use random assignment to select participants to a group.Measure the same dependent variable in each group. Use inferential statistics to compare differences between groups. | |
C10483 | The main nonparametric tests are:1-sample sign test. 1-sample Wilcoxon signed rank test. Friedman test. Goodman Kruska's Gamma: a test of association for ranked variables.Kruskal-Wallis test. The Mann-Kendall Trend Test looks for trends in time-series data.Mann-Whitney test. Mood's Median test.More items• | |
C10484 | Statistical conclusion validity is the degree to which conclusions about the relationship among variables based on the data are correct or "reasonable". Statistical conclusion validity involves ensuring the use of adequate sampling procedures, appropriate statistical tests, and reliable measurement procedures. | |
C10485 | (It is also possible to integrate existing knowledge with data - one way of doing that is using Bayesian statistics, in fact!) Since Bayesian statistics provides a framework for updating "knowledge", it is in fact used a whole lot in machine learning. | |
C10486 | A hierarchical linear regression is a special form of a multiple linear regression analysis in which more variables are added to the model in separate steps called “blocks.” This is often done to statistically “control” for certain variables, to see whether adding variables significantly improves a model's ability to | |
C10487 | What is the best way to store data used for Natural Language Processing?stream data from disk when you can. inverted indexes each get their own text file (more relevant for search, maybe not what you're doing)use sparse data structures (e.g. sparse matrix) as much as possible when you need to load stuff into memory. | |
C10488 | Sampling distributions are important for inferential statistics. In practice, one will collect sample data and, from these data, estimate parameters of the population distribution. Thus, knowledge of the sampling distribution can be very useful in making inferences about the overall population. | |
C10489 | An artificial neural network (ANN) is the piece of a computing system designed to simulate the way the human brain analyzes and processes information. ANNs have self-learning capabilities that enable them to produce better results as more data becomes available. | |
C10490 | The mean, median, and mode of a normal distribution are equal. The area under the normal curve is equal to 1.0. Normal distributions are denser in the center and less dense in the tails. Normal distributions are defined by two parameters, the mean (μ) and the standard deviation (σ). | |
C10491 | • h is the Vapnik Chervonenkis (VC) dimension and is a measure of the capacity or complexity of the machine. | |
C10492 | Diana Borsa, Bilal Piot, Rémi Munos, Olivier Pietquin. Download PDF. Observational learning is a type of learning that occurs as a function of observing, retaining and possibly replicating or imitating the behaviour of another agent. | |
C10493 | Loading MNIST handwritten digits datasetLoading MNIST handwritten digits dataset. Loading the MNIST dataset.Introduction. Required Libraries. scikit-learn: fetch_mldata. Check the folder structure. Download and store the dataset in local. Load the dataset.Finally, the variable "mnist" will contain the data!More items• | |
C10494 | Path analysis is a special case of SEM. Most of the models that you will see in the literature are SEM rather than path analyses. The main difference between the two types of models is that path analysis assumes that all variables are measured without error. SEM uses latent variables to account for measurement error. | |
C10495 | 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." | |
C10496 | Sparse signals are characterized by a few nonzero coefficients in one of their transformation domains. This was the main premise in designing signal compression algorithms. Compressive sensing as a new approach employs the sparsity property as a precondition for signal recovery. | |
C10497 | Most websites like Amazon, YouTube, and Netflix use collaborative filtering as a part of their sophisticated recommendation systems. You can use this technique to build recommenders that give suggestions to a user on the basis of the likes and dislikes of similar users. | |
C10498 | In its initial test, the Altman Z-Score was found to be 72% accurate in predicting bankruptcy two years prior to the event. In subsequent tests over 31 years up until 1999, the model was found to be 80-90% accurate in predicting bankruptcy one year prior to the event. | |
C10499 | four types |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.