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C7400 | The binomial theorem is an algebraic method of expanding a binomial expression. Essentially, it demonstrates what happens when you multiply a binomial by itself (as many times as you want). For example, consider the expression (4x+y)7 ( 4 x + y ) 7 . | |
C7401 | In statistics, the likelihood-ratio test assesses the goodness of fit of two competing statistical models based on the ratio of their likelihoods, specifically one found by maximization over the entire parameter space and another found after imposing some constraint. | |
C7402 | Here is a simpler rule: If two SEM error bars do overlap, and the sample sizes are equal or nearly equal, then you know that the P value is (much) greater than 0.05, so the difference is not statistically significant. If the sample sizes are very different, this rule of thumb does not always work. | |
C7403 | Most implementations of random forest (and many other machine learning algorithms) that accept categorical inputs are either just automating the encoding of categorical features for you or using a method that becomes computationally intractable for large numbers of categories. A notable exception is H2O. | |
C7404 | In particular, a random experiment is a process by which we observe something uncertain. After the experiment, the result of the random experiment is known. An outcome is a result of a random experiment. The set of all possible outcomes is called the sample space. | |
C7405 | The best fit line is the one that minimises sum of squared differences between actual and estimated results. Taking average of minimum sum of squared difference is known as Mean Squared Error (MSE). Smaller the value, better the regression model. | |
C7406 | Adam is a replacement optimization algorithm for stochastic gradient descent for training deep learning models. Adam combines the best properties of the AdaGrad and RMSProp algorithms to provide an optimization algorithm that can handle sparse gradients on noisy problems. | |
C7407 | Joint probability is the probability of two events occurring simultaneously. Marginal probability is the probability of an event irrespective of the outcome of another variable. Conditional probability is the probability of one event occurring in the presence of a second event. | |
C7408 | Asymptotic Analysis is the big idea that handles above issues in analyzing algorithms. In Asymptotic Analysis, we evaluate the performance of an algorithm in terms of input size (we don't measure the actual running time). We calculate, how the time (or space) taken by an algorithm increases with the input size. | |
C7409 | Each sample contains different elements so the value of the sample statistic differs for each sample selected. These statistics provide different estimates of the parameter. The sampling distribution describes how these different values are distributed. | |
C7410 | Clustering and Association are two types of Unsupervised learning. Important clustering types are: 1)Hierarchical clustering 2) K-means clustering 3) K-NN 4) Principal Component Analysis 5) Singular Value Decomposition 6) Independent Component Analysis. | |
C7411 | The three main metrics used to evaluate a classification model are accuracy, precision, and recall. Accuracy is defined as the percentage of correct predictions for the test data. It can be calculated easily by dividing the number of correct predictions by the number of total predictions. | |
C7412 | The standard error can be used to gauge the precision of a statistical estimate or to permit a judgement being made of the divergence between expected and observed values. clearly the concept of standard error of an estimate and its various uses in practice. | |
C7413 | Systematic errors are biases in measurement which lead to a situation wherein the mean of many separate measurements differs significantly from the actual value of the measured attribute in one direction. A systematic error makes the measured value always smaller or larger than the true value, but not both. | |
C7414 | So, to find the residual I would subtract the predicted value from the measured value so for x-value 1 the residual would be 2 - 2.6 = -0.6. Mentor: That is right! The residual of the independent variable x=1 is -0.6. | |
C7415 | Here are 11 tips for making the most of your large data sets.Cherish your data. “Keep your raw data raw: don't manipulate it without having a copy,” says Teal. Visualize the information.Show your workflow. Use version control. Record metadata. Automate, automate, automate. Make computing time count. Capture your environment.More items• | |
C7416 | Classification SVM Type 1 (also known as C-SVM classification); Classification SVM Type 2 (also known as nu-SVM classification); Regression SVM Type 1 (also known as epsilon-SVM regression); Regression SVM Type 2 (also known as nu-SVM regression). | |
C7417 | Convenience sampling (also known as grab sampling, accidental sampling, or opportunity sampling) is a type of non-probability sampling that involves the sample being drawn from that part of the population that is close to hand. This type of sampling is most useful for pilot testing. | |
C7418 | Linear Regression Is Limited to Linear Relationships By its nature, linear regression only looks at linear relationships between dependent and independent variables. That is, it assumes there is a straight-line relationship between them. | |
C7419 | A derivative is a continuous description of how a function changes with small changes in one or multiple variables. We're going to look into many aspects of that statement. For example. | |
C7420 | Regression analysis is a form of inferential statistics. The p-values help determine whether the relationships that you observe in your sample also exist in the larger population. The p-value for each independent variable tests the null hypothesis that the variable has no correlation with the dependent variable. | |
C7421 | From Simple English Wikipedia, the free encyclopedia. The entropy of an object is a measure of the amount of energy which is unavailable to do work. Entropy is also a measure of the number of possible arrangements the atoms in a system can have. In this sense, entropy is a measure of uncertainty or randomness. | |
C7422 | Sampling Distribution of Sample Variance This is the variance of the population. The variance of this sampling distribution can be computed by finding the expected value of the square of the sample variance and subtracting the square of 2.92. | |
C7423 | The chi-square distribution is used in the common chi-square tests for goodness of fit of an observed distribution to a theoretical one, the independence of two criteria of classification of qualitative data, and in confidence interval estimation for a population standard deviation of a normal distribution from a | |
C7424 | Yes, this is possible and I have heard it termed as joint regression or multivariate regression. Regression analysis involving more than one independent variable and more than one dependent variable is indeed (also) called multivariate regression. This methodology is technically known as canonical correlation analysis. | |
C7425 | Ensemble learning is the process by which multiple models, such as classifiers or experts, are strategically generated and combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.) | |
C7426 | The quantizing of an analog signal is done by discretizing the signal with a number of quantization levels. Quantization is representing the sampled values of the amplitude by a finite set of levels, which means converting a continuous-amplitude sample into a discrete-time signal. | |
C7427 | An SVM performs classification tasks by constructing hyperplanes in a multidimensional space that separates cases of different class labels. You can use an SVM when your data has exactly two classes, e.g. binary classification problems, but in this article we'll focus on a multi-class support vector machine in R. | |
C7428 | The k-means clustering algorithm uses the Euclidean distance [1,4] to measure the similarities between objects. Both iterative algorithm and adaptive algorithm exist for the standard k-means clustering. K-means clustering algorithms need to assume that the number of groups (clusters) is known a priori. | |
C7429 | Loss is the penalty for a bad prediction. That is, loss is a number indicating how bad the model's prediction was on a single example. If the model's prediction is perfect, the loss is zero; otherwise, the loss is greater. | |
C7430 | The most popular is definitely KMP, if you need fast string matching without any particular usecase in mind it's what you should use. Here are your options(with time complexity): Brute Force O(nm) Knuth–Morris–Pratt algorithm - O(n) | |
C7431 | Geoffrey HintonGeoffrey Hinton CC FRS FRSCHinton in 2013BornGeoffrey Everest Hinton 6 December 1947 Wimbledon, LondonAlma materUniversity of Cambridge (BA) University of Edinburgh (PhD)Known forApplications of Backpropagation Boltzmann machine Deep learning Capsule neural network10 more rows | |
C7432 | Logistic regression is basically a supervised classification algorithm. In a classification problem, the target variable(or output), y, can take only discrete values for given set of features(or inputs), X. Contrary to popular belief, logistic regression IS a regression model. | |
C7433 | A threshold transfer function is sometimes used to quantify the output of a neuron in the output layer. All possible connections between neurons are allowed. Since loops are present in this type of network, it becomes a non-linear dynamic system which changes continuously until it reaches a state of equilibrium. | |
C7434 | The normal distribution is a probability distribution. As with any probability distribution, the proportion of the area that falls under the curve between two points on a probability distribution plot indicates the probability that a value will fall within that interval. | |
C7435 | Advertisements. Bayesian classification is based on Bayes' Theorem. Bayesian classifiers are the statistical classifiers. Bayesian classifiers can predict class membership probabilities such as the probability that a given tuple belongs to a particular class. | |
C7436 | Significance levels The convention in most biological research is to use a significance level of 0.05. This means that if the P value is less than 0.05, you reject the null hypothesis; if P is greater than or equal to 0.05, you don't reject the null hypothesis. | |
C7437 | The lower quartile, or first quartile, is denoted as Q1 and is the middle number that falls between the smallest value of the dataset and the median. The second quartile, Q2, is also the median. | |
C7438 | A discrete distribution is one in which the data can only take on certain values, for example integers. A continuous distribution is one in which data can take on any value within a specified range (which may be infinite). | |
C7439 | The equation of a hyperplane is w · x + b = 0, where w is a vector normal to the hyperplane and b is an offset. | |
C7440 | In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon being observed. Choosing informative, discriminating and independent features is a crucial step for effective algorithms in pattern recognition, classification and regression. | |
C7441 | In deep learning, a convolutional neural network (CNN, or ConvNet) is a class of deep neural networks, most commonly applied to analyzing visual imagery. Convolutional networks were inspired by biological processes in that the connectivity pattern between neurons resembles the organization of the animal visual cortex. | |
C7442 | Another most important role of training data for machine learning is classifying the data sets into various categorized which is very much important for supervised machine learning. It helps them to recognize and classify the similar objects in future, thus training data is very important for such classification. | |
C7443 | The equation of a hyperplane is w · x + b = 0, where w is a vector normal to the hyperplane and b is an offset. | |
C7444 | As long as the growth factor used is assumed to be normally distributed (as we assume with the rate of return), then the lognormal distribution makes sense. Normal distribution cannot be used to model stock prices because it has a negative side, and stock prices cannot fall below zero. | |
C7445 | Representation is basically the space of allowed models (the hypothesis space), but also takes into account the fact that we are expressing models in some formal language that may encode some models more easily than others (even within that possible set). | |
C7446 | Definition: In simple words, data mining is defined as a process used to extract usable data from a larger set of any raw data. It implies analysing data patterns in large batches of data using one or more software. Data mining is also known as Knowledge Discovery in Data (KDD). | |
C7447 | Predictive ModelingClean the data by removing outliers and treating missing data.Identify a parametric or nonparametric predictive modeling approach to use.Preprocess the data into a form suitable for the chosen modeling algorithm.Specify a subset of the data to be used for training the model.More items | |
C7448 | Feature Extraction aims to reduce the number of features in a dataset by creating new features from the existing ones (and then discarding the original features). These new reduced set of features should then be able to summarize most of the information contained in the original set of features. | |
C7449 | The type of inference exhibited here is called abduction or, somewhat more commonly nowadays, Inference to the Best Explanation.1.1 Deduction, induction, abduction. Abduction is normally thought of as being one of three major types of inference, the other two being deduction and induction. 1.2 The ubiquity of abduction. | |
C7450 | Top reasons to use feature selection are: It enables the machine learning algorithm to train faster. It reduces the complexity of a model and makes it easier to interpret. It improves the accuracy of a model if the right subset is chosen. | |
C7451 | In cluster sampling, researchers divide a population into smaller groups known as clusters.You thus decide to use the cluster sampling method.Step 1: Define your population. Step 2: Divide your sample into clusters. Step 3: Randomly select clusters to use as your sample. Step 4: Collect data from the sample. | |
C7452 | In computer science and machine learning, pattern recognition is a technology that matches the information stored in the database with the incoming data. | |
C7453 | The relationship between correlation coefficient and a scatterplot is that the two of them describe how similar the variables are. A scatterplot that looks like a blobby thing without direction has a correlation coefficient closer to 0, meaning the two variables aren't correlated. | |
C7454 | TensorFlow applications can be run on most any target that's convenient: a local machine, a cluster in the cloud, iOS and Android devices, CPUs or GPUs. If you use Google's own cloud, you can run TensorFlow on Google's custom TensorFlow Processing Unit (TPU) silicon for further acceleration. | |
C7455 | The normal distribution is the most important probability distribution in statistics because it fits many natural phenomena. For example, heights, blood pressure, measurement error, and IQ scores follow the normal distribution. It is also known as the Gaussian distribution and the bell curve. | |
C7456 | Semi-supervised learning takes a middle ground. It uses a small amount of labeled data bolstering a larger set of unlabeled data. And reinforcement learning trains an algorithm with a reward system, providing feedback when an artificial intelligence agent performs the best action in a particular situation. | |
C7457 | AlphaGo Zero is a version of DeepMind's Go software AlphaGo. By playing games against itself, AlphaGo Zero surpassed the strength of AlphaGo Lee in three days by winning 100 games to 0, reached the level of AlphaGo Master in 21 days, and exceeded all the old versions in 40 days. | |
C7458 | How to Perform Systematic Sampling: StepsStep 1: Assign a number to every element in your population. Step 2: Decide how large your sample size should be. Step 3: Divide the population by your sample size. Step 1: Assign a number to every element in your population.Step 2: Decide how large your sample size should be.More items• | |
C7459 | A type of research design where one sample is drawn from the population of interest only once. | |
C7460 | I guess if you squint at it sideways, binary search is greedy in the sense that you're trying to cut down your search space by as much as you can in each step. It just happens to be a greedy algorithm in a search space with structure making that both efficient, and always likely to find the right answer. | |
C7461 | Moran's I is a correlation coefficient that measures the overall spatial autocorrelation of your data set. In other words, it measures how one object is similar to others surrounding it. If objects are attracted (or repelled) by each other, it means that the observations are not independent. | |
C7462 | 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. | |
C7463 | In machine learning, hyperparameter optimization or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. A hyperparameter is a parameter whose value is used to control the learning process. By contrast, the values of other parameters (typically node weights) are learned. | |
C7464 | Averaging Likert Responses Because Likert and Likert-like survey questions are neatly ordered with numerical responses, it's easy and tempting to average them by adding the numeric value of each response, and then dividing by the number of respondents. | |
C7465 | A multi-agent system (MAS or "self-organized system") is a computerized system composed of multiple interacting intelligent agents. Intelligence may include methodic, functional, procedural approaches, algorithmic search or reinforcement learning. | |
C7466 | Exploratory structural equation modeling (ESEM) is an approach for analysis of latent variables using exploratory factor analysis to evaluate the measurement model. ESEM is recommended when non-ignorable cross-factor loadings exist. | |
C7467 | Categories with a large difference between observed and expected values make a larger contribution to the overall chi-square statistic. In these results, the contribution values from each category sum to the overall chi-square statistic, which is 0.65. | |
C7468 | For quick and visual identification of a normal distribution, use a QQ plot if you have only one variable to look at and a Box Plot if you have many. Use a histogram if you need to present your results to a non-statistical public. As a statistical test to confirm your hypothesis, use the Shapiro Wilk test. | |
C7469 | Regularization is a technique which makes slight modifications to the learning algorithm such that the model generalizes better. This in turn improves the model's performance on the unseen data as well. | |
C7470 | Data are rarely randomly distributed in high-dimensions and are highly correlated, often with spurious correlations. The distances between a data point and its nearest and farthest neighbours can become equidistant in high dimensions, potentially compromising the accuracy of some distance-based analysis tools. | |
C7471 | Lift can be found by dividing the confidence by the unconditional probability of the consequent, or by dividing the support by the probability of the antecedent times the probability of the consequent, so: The lift for Rule 1 is (3/4)/(4/7) = (3*7)/(4 * 4) = 21/16 ≈ 1.31. | |
C7472 | Divisive Clustering: The divisive clustering algorithm is a top-down clustering approach, initially, all the points in the dataset belong to one cluster and split is performed recursively as one moves down the hierarchy. | |
C7473 | In clustering, a group of different data objects is classified as similar objects. One group means a cluster of data. Data sets are divided into different groups in the cluster analysis, which is based on the similarity of the data. After the classification of data into various groups, a label is assigned to the group. | |
C7474 | If the mean more accurately represents the center of the distribution of your data, and your sample size is large enough, use a parametric test. If the median more accurately represents the center of the distribution of your data, use a nonparametric test even if you have a large sample size. | |
C7475 | A control problem involves a system that is described by state variables. The problem is to find a time control stratergy to make the system reach the terget state that is find conditions for application of force as a function of the control variables of the system (V,W,Th). | |
C7476 | Neural Networks are networks used in Machine Learning that work similar to the human nervous system. It is designed to function like the human brain where many things are connected in various ways. There are many kinds of artificial neural networks used for the computational model. | |
C7477 | There is no plausible way for the brain to use backpropagation. The way in which neurons connect and communicate in the brain do not allow any mechanism that could accommodate the backpropagation principle. | |
C7478 | The standard deviation is a statistic that measures the dispersion of a dataset relative to its mean and is calculated as the square root of the variance. If the data points are further from the mean, there is a higher deviation within the data set; thus, the more spread out the data, the higher the standard deviation. | |
C7479 | A Convolutional neural network (CNN) is a neural network that has one or more convolutional layers and are used mainly for image processing, classification, segmentation and also for other auto correlated data. A convolution is essentially sliding a filter over the input. | |
C7480 | Abstract. Markov chain Monte Carlo (MCMC) is a simulation technique that can be used to find the posterior distribution and to sample from it. Thus, it is used to fit a model and to draw samples from the joint posterior distribution of the model parameters. The software OpenBUGS and Stan are MCMC samplers. | |
C7481 | The backpropagation algorithm works by computing the gradient of the loss function with respect to each weight by the chain rule, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this is an example of dynamic | |
C7482 | The level of statistical significance is often expressed as a p-value between 0 and 1. The smaller the p-value, the stronger the evidence that you should reject the null hypothesis. A p-value higher than 0.05 (> 0.05) is not statistically significant and indicates strong evidence for the null hypothesis. | |
C7483 | Decision trees help you to evaluate your options. Decision Trees are excellent tools for helping you to choose between several courses of action. They provide a highly effective structure within which you can lay out options and investigate the possible outcomes of choosing those options. | |
C7484 | Change Detection means updating the DOM whenever data is changed. In its default strategy, whenever any data is mutated or changed, Angular will run the change detector to update the DOM. In the onPush strategy, Angular will only run the change detector when a new reference is passed to @Input() data. | |
C7485 | Tensor Processing Unit (TPU) is an AI accelerator application-specific integrated circuit (ASIC) developed by Google specifically for neural network machine learning, particularly using Google's own TensorFlow software. | |
C7486 | The F distribution is the probability distribution associated with the f statistic. In this lesson, we show how to compute an f statistic and how to find probabilities associated with specific f statistic values. | |
C7487 | In machine learning and statistics, the learning rate is a tuning parameter in an optimization algorithm that determines the step size at each iteration while moving toward a minimum of a loss function. In setting a learning rate, there is a trade-off between the rate of convergence and overshooting. | |
C7488 | Simple Linear Regression Math by HandCalculate average of your X variable.Calculate the difference between each X and the average X.Square the differences and add it all up. Calculate average of your Y variable.Multiply the differences (of X and Y from their respective averages) and add them all together.More items | |
C7489 | A Poisson process is a non-deterministic process where events occur continuously and independently of each other. A Poisson distribution is a discrete probability distribution that represents the probability of events (having a Poisson process) occurring in a certain period of time. | |
C7490 | Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. Descriptive statistics are typically distinguished from inferential statistics. With descriptive statistics you are simply describing what is or what the data shows. | |
C7491 | A posterior probability value is a prior probability value that has been a | Course Hero. Study Resources. by Textbook. by Literature Title. | |
C7492 | 7 Types of Classification AlgorithmsLogistic Regression.Naïve Bayes.Stochastic Gradient Descent.K-Nearest Neighbours.Decision Tree.Random Forest.Support Vector Machine. | |
C7493 | Although side effects believed to be caused by statins can be annoying, consider the benefits of taking a statin before you decide to stop taking your medication. Remember that statin medications can reduce your risk of a heart attack or stroke, and the risk of life-threatening side effects from statins is very low. | |
C7494 | 5.2 Selector syntax A simple selector is either a type selector or universal selector followed immediately by zero or more attribute selectors, ID selectors, or pseudo-classes, in any order. The simple selector matches if all of its components match. | |
C7495 | Characteristics of an AlgorithmUnambiguous − Algorithm should be clear and unambiguous. Input − An algorithm should have 0 or more well-defined inputs.Output − An algorithm should have 1 or more well-defined outputs, and should match the desired output.More items | |
C7496 | Sigmoid and tanh should not be used as activation function for the hidden layer. This is because of the vanishing gradient problem, i.e., if your input is on a higher side (where sigmoid goes flat) then the gradient will be near zero. The best function for hidden layers is thus ReLu. | |
C7497 | We find the robust standard deviation estimate by multiplying the MAD by a factor that happens to have a value close to 1.5. This gives us a robust value ('sigma- hat') of B . . If we use this method on data without outliers, it provides estimates that are close to x and s, so no harm is done. | |
C7498 | Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. Machine Learning is a current application of AI based around the idea that we should really just be able to give machines access to data and let them learn for themselves. | |
C7499 | If you have only one independent variable, R-squared(R2) remains the same. Because in single variable linear model - R2 is nothing but a square of the correlation between two variables. |
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