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C4700 | Differential calculus is usually taught first. I think most students find it more intuitive because they deal with rates of change in real life. Integral calculus is more abstract, and indefinite integrals are much easier to evaluate if you understand differentiation. | |
C4701 | “Goodness of Fit” of a linear regression model attempts to get at the perhaps sur- prisingly tricky issue of how well a model fits a given set of data, or how well it will predict a future set of observations. | |
C4702 | In word2vec, you train to find word vectors and then run similarity queries between words. In doc2vec, you tag your text and you also get tag vectors. If two authors generally use the same words then their vector will be closer. | |
C4703 | In summary, model parameters are estimated from data automatically and model hyperparameters are set manually and are used in processes to help estimate model parameters. Model hyperparameters are often referred to as parameters because they are the parts of the machine learning that must be set manually and tuned. | |
C4704 | 3:0713:58Suggested clip · 114 secondsSurvival Analysis in R - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C4705 | A moment of a probability function taken about 0, (1) (2) The raw moments (sometimes also called "crude moments") can be expressed as terms of the central moments (i.e., those taken about the mean ) using the inverse binomial transform. | |
C4706 | Precision and recall can be combined into a single score that seeks to balance both concerns, called the F-score or the F-measure. The F-Measure is a popular metric for imbalanced classification. | |
C4707 | 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. | |
C4708 | The distribution becomes normal when you have several different forces of varying magnitude acting together. Generally, the more forces then the more normal the distribution will become. This occurs a lot in nature which is why the normal distribution is so prevalent. | |
C4709 | Look at current performance data to establish a baseline and benchmark for improvement.Form a hypothesis. An A/B hypothesis is an assumption on which to base the test. Design and run the test. Create the two versions to test, A and B. Analyze the results. Implement the results. | |
C4710 | The three different ways of feature extraction are horizontal direction, vertical direction and diagonal direction. Recognition rate percentage for vertical, horizontal and diagonal based feature extraction using feed forward back propagation neural network as classification phase are 92.69, 93.68, 97.80 respectively. | |
C4711 | Linear regression quantifies the relationship between one or more predictor variable(s) and one outcome variable. For example, it can be used to quantify the relative impacts of age, gender, and diet (the predictor variables) on height (the outcome variable). | |
C4712 | This measure is represented as a value between 0.0 and 1.0, where a value of 1.0 indicates a perfect fit, and is thus a highly reliable model for future forecasts, while a value of 0.0 would indicate that the model fails to accurately model the data at all. | |
C4713 | There are two possible objectives in a discriminant analysis: finding a predictive equation for classifying new individuals or interpreting the predictive equation to better understand the relationships that may exist among the variables. In many ways, discriminant analysis parallels multiple regression analysis. | |
C4714 | 2. HIDDEN MARKOV MODELS. A hidden Markov model (HMM) is a statistical model that can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable. We call the observed event a `symbol' and the invisible factor underlying the observation a `state'. | |
C4715 | In machine learning, a “kernel” is usually used to refer to the kernel trick, a method of using a linear classifier to solve a non-linear problem. It entails transforming linearly inseparable data like (Fig. 3) to linearly separable ones (Fig. 2). | |
C4716 | Handling overfittingReduce the network's capacity by removing layers or reducing the number of elements in the hidden layers.Apply regularization , which comes down to adding a cost to the loss function for large weights.Use Dropout layers, which will randomly remove certain features by setting them to zero. | |
C4717 | A sampling distribution is a probability distribution of a statistic obtained from a larger number of samples drawn from a specific population. The sampling distribution of a given population is the distribution of frequencies of a range of different outcomes that could possibly occur for a statistic of a population. | |
C4718 | From our confusion matrix, we can calculate five different metrics measuring the validity of our model.Accuracy (all correct / all) = TP + TN / TP + TN + FP + FN.Misclassification (all incorrect / all) = FP + FN / TP + TN + FP + FN.Precision (true positives / predicted positives) = TP / TP + FP.More items | |
C4719 | The inverse CDF technique for generating a random sample uses the fact that a continuous CDF, F, is a one-to-one mapping of the domain of the CDF into the interval (0,1). Therefore, if U is a uniform random variable on (0,1), then X = F–1(U) has the distribution F. | |
C4720 | They are often confused with each other. The 'K' in K-Means Clustering has nothing to do with the 'K' in KNN algorithm. k-Means Clustering is an unsupervised learning algorithm that is used for clustering whereas KNN is a supervised learning algorithm used for classification. | |
C4721 | An algorithm is said to be constant time (also written as O(1) time) if the value of T(n) is bounded by a value that does not depend on the size of the input. For example, accessing any single element in an array takes constant time as only one operation has to be performed to locate it. | |
C4722 | In neural image captioning systems, a recurrent neural network (RNN) is typically viewed as the primary `generation' component. This view suggests that the RNN should only be used to encode linguistic features and that only the final representation should be `merged' with the image features at a later stage. | |
C4723 | The t distribution is therefore leptokurtic. The t distribution approaches the normal distribution as the degrees of freedom increase. Since the t distribution is leptokurtic, the percentage of the distribution within 1.96 standard deviations of the mean is less than the 95% for the normal distribution. | |
C4724 | Marginal distributions are P(X = x), P(Y = y). | |
C4725 | Most statisticians agree that the minimum sample size to get any kind of meaningful result is 100. If your population is less than 100 then you really need to survey all of them. | |
C4726 | The distribution of a variable is a description of the relative numbers of times each possible outcome will occur in a number of trials. If the measure is a Radon measure (which is usually the case), then the statistical distribution is a generalized function in the sense of a generalized function. | |
C4727 | k-means clustering | |
C4728 | In a dataset a training set is implemented to build up a model, while a test (or validation) set is to validate the model built. Data points in the training set are excluded from the test (validation) set. | |
C4729 | 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. | |
C4730 | Taguchi loss function formulaL is the loss function.y is the value of the characteristic you are measuring (e.g. length of product)m is the value you are aiming for (in our example, perfect length for the product)k is a proportionality constant (i.e. just a number) | |
C4731 | A probability distribution is a statistical function that describes all the possible values and likelihoods that a random variable can take within a given range. These factors include the distribution's mean (average), standard deviation, skewness, and kurtosis. | |
C4732 | Logistic regression is known and used as a linear classifier. It is used to come up with a hyperplane in feature space to separate observations that belong to a class from all the other observations that do not belong to that class. The decision boundary is thus linear.13/03/2019 | |
C4733 | Univariate is a term commonly used in statistics to describe a type of data which consists of observations on only a single characteristic or attribute. A simple example of univariate data would be the salaries of workers in industry. | |
C4734 | Batch processing requires separate programs for input, process and output. In contrast, real time data processing involves a continual input, process and output of data. Data must be processed in a small time period (or near real time). Radar systems, customer services and bank ATMs are examples. | |
C4735 | In the statistical analysis of time series, autoregressive–moving-average (ARMA) models provide a parsimonious description of a (weakly) stationary stochastic process in terms of two polynomials, one for the autoregression (AR) and the second for the moving average (MA). | |
C4736 | We can use the regression line to predict values of Y given values of X. For any given value of X, we go straight up to the line, and then move horizontally to the left to find the value of Y. The predicted value of Y is called the predicted value of Y, and is denoted Y'. | |
C4737 | All of these, in different ways, involve hierarchical representation of data. Lists - linked lists are used to represent hierarchical knowledge. Trees - graphs which represent hierarchical knowledge. LISP, the main programming language of AI, was developed to process lists and trees. | |
C4738 | Correlation is a statistical measure that expresses the extent to which two variables are linearly related (meaning they change together at a constant rate). | |
C4739 | A time series is a sequence of numerical data points in successive order. In investing, a time series tracks the movement of the chosen data points, such as a security's price, over a specified period of time with data points recorded at regular intervals. | |
C4740 | The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. The number of hidden neurons should be less than twice the size of the input layer. | |
C4741 | 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. | |
C4742 | Group projects, discussions, and writing are examples of active learning, because they involve doing something. | |
C4743 | Examples of such greedy algorithms are Kruskal's algorithm and Prim's algorithm for finding minimum spanning trees, and the algorithm for finding optimum Huffman trees. | |
C4744 | Hebb proposed a mechanism to update weights between neurons in a neural network. This method of weight updation enabled neurons to learn and was named as Hebbian Learning. Information is stored in the connections between neurons in neural networks, in the form of weights. | |
C4745 | The sampling distribution of the sample mean is very useful because it can tell us the probability of getting any specific mean from a random sample. | |
C4746 | In statistics, normality tests are used to determine if a data set is well-modeled by a normal distribution and to compute how likely it is for a random variable underlying the data set to be normally distributed. | |
C4747 | The power of Hypothesis test is the probability of rejecting null hypothesis . As stated above we may commit Type I and Type II errors while testing a hypothesis. Accordingly 1 – b value is the measure of how well the test is working or what is technically described as the power of the test. | |
C4748 | In the presence of heteroskedasticity, there are two main consequences on the least squares estimators: The least squares estimator is still a linear and unbiased estimator, but it is no longer best. That is, there is another estimator with a smaller variance. | |
C4749 | "A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph." It is also called a Bayes network, belief network, decision network, or Bayesian model. | |
C4750 | Plot a symbol at the median and draw a box between the lower and upper quartiles. Calculate the interquartile range (the difference between the upper and lower quartile) and call it IQ. The line from the lower quartile to the minimum is now drawn from the lower quartile to the smallest point that is greater than L1. | |
C4751 | Univariate analysis has the purpose to describe a single variable distribution in one sample. It is the first important step of every clinical trial. | |
C4752 | The error function and its approximations can be used to estimate results that hold with high probability or with low probability. Given random variable and constant : where A and B are certain numeric constants. If L is sufficiently far from the mean, i.e. , then: so the probability goes to 0 as . | |
C4753 | High coefficient value means the variable is playing a major role in deciding the boundary (in case of logistic). Odds ratio tells the changes produced in output variable per unit change in that particular input variable. For the relation between both, odd ratio r = exp(coefficient). | |
C4754 | Experience replay is the fundamental data generating mech- anism in off-policy deep reinforcement learning (Lin, 1992). It has been shown to improve sample efficiency and stability. by storing a fixed number of the most recently collected. transitions for training. | |
C4755 | When n * p and n * q are greater than 5, you can use the normal approximation to the binomial to solve a problem. | |
C4756 | All Answers (6) Indeed a common rule of thumb is 10 outcome events per predictor, but sometimes this rule is too conservative and can be relaxed (see Vittinghoff E, McCulloch CE. 2007. Relaxing the rule of ten events per variable in logistic and Cox regression. | |
C4757 | KNN represents a supervised classification algorithm that will give new data points accordingly to the k number or the closest data points, while k-means clustering is an unsupervised clustering algorithm that gathers and groups data into k number of clusters. | |
C4758 | Advantages of Mini-Batch Gradient Descent Stable Convergence: Another advantage is the more stable converge towards the global minimum since we calculate an average gradient over n samples that results in less noise. | |
C4759 | Cost Function It is a function that measures the performance of a Machine Learning model for given data. Cost Function quantifies the error between predicted values and expected values and presents it in the form of a single real number. Depending on the problem Cost Function can be formed in many different ways. | |
C4760 | The t-value measures the size of the difference relative to the variation in your sample data. Put another way, T is simply the calculated difference represented in units of standard error. The greater the magnitude of T, the greater the evidence against the null hypothesis. | |
C4761 | Some of my suggestions to you would be:Feature Scaling and/or Normalization - Check the scales of your gre and gpa features. Class Imbalance - Look for class imbalance in your data. Optimize other scores - You can optimize on other metrics also such as Log Loss and F1-Score.More items | |
C4762 | a survey of high school students to measure teenage use of illegal drugs will be a biased sample because it does not include home-schooled students or dropouts. A sample is also biased if certain members are underrepresented or overrepresented relative to others in the population. | |
C4763 | The effect of Gaussian smoothing is to blur an image, in a similar fashion to the mean filter. The degree of smoothing is determined by the standard deviation of the Gaussian. (Larger standard deviation Gaussians, of course, require larger convolution kernels in order to be accurately represented.) | |
C4764 | Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream. | |
C4765 | In probability theory and statistics, two real-valued random variables, , , are said to be uncorrelated if their covariance, , is zero. If two variables are uncorrelated, there is no linear relationship between them. | |
C4766 | If a vector is perpendicular to a basis of a plane, then it is perpendicular to that entire plane. So, the cross product of two (linearly independent) vectors, since it is orthogonal to each, is orthogonal to the plane which they span. | |
C4767 | After the SBI PO selection process is over the shortlisted candidates will be posted as “Probationary Officers” in SBI partner branches and will be on probation period for two years. | |
C4768 | Multiple linear regression (MLR), also known simply as multiple regression, is a statistical technique that uses several explanatory variables to predict the outcome of a response variable. Multiple regression is an extension of linear (OLS) regression that uses just one explanatory variable. | |
C4769 | With supervised learning, you have features and labels. The features are the descriptive attributes, and the label is what you're attempting to predict or forecast. | |
C4770 | A decision tree is simply a set of cascading questions. When you get a data point (i.e. set of features and values), you use each attribute (i.e. a value of a given feature of the data point) to answer a question. The answer to each question decides the next question. | |
C4771 | Some use cases for unsupervised learning — more specifically, clustering — include: Customer segmentation, or understanding different customer groups around which to build marketing or other business strategies. Genetics, for example clustering DNA patterns to analyze evolutionary biology. | |
C4772 | Student's t Distribution. The t distribution (aka, Student's t-distribution) is a probability distribution that is used to estimate population parameters when the sample size is small and/or when the population variance is unknown. | |
C4773 | An estimate of a population parameter may be expressed in two ways: Point estimate. A point estimate of a population parameter is a single value of a statistic. For example, the sample mean x is a point estimate of the population mean μ. | |
C4774 | Linear regression is used to predict the continuous dependent variable using a given set of independent variables. Logistic Regression is used to predict the categorical dependent variable using a given set of independent variables. The output for Linear Regression must be a continuous value, such as price, age, etc. | |
C4775 | Student's t-test assumes that the two population(being compared) distributions are normally distributed with equal variance. Welch's t-test is designed for unequal sample distribution variance, but the assumption of sample distribution normality is maintained. | |
C4776 | String theory has not failed, and there has been progress since 1999. It's just that it's a pretty abstract field of research, so it's hard to describe the recent progress in an accessible and understandable way. | |
C4777 | The law of large numbers is a theorem from probability and statistics that suggests that the average result from repeating an experiment multiple times will better approximate the true or expected underlying result. The law of large numbers explains why casinos always make money in the long run. | |
C4778 | There are four types of classification. They are Geographical classification, Chronological classification, Qualitative classification, Quantitative classification. | |
C4779 | A measure of central tendency is a single value that attempts to describe a set of data by identifying the central position within that set of data. The mean (often called the average) is most likely the measure of central tendency that you are most familiar with, but there are others, such as the median and the mode. | |
C4780 | A blurring filter where you move over the image with a box filter (all the same values in the window) is an example of a linear filter. A non-linear filter is one that cannot be done with convolution or Fourier multiplication. A sliding median filter is a simple example of a non-linear filter. | |
C4781 | Descriptive, prescriptive, and normative are three main areas of decision theory and each studies a different type of decision making. | |
C4782 | It is able to do this by using a novel form of reinforcement learning, in which AlphaGo Zero becomes its own teacher. The system starts off with a neural network that knows nothing about the game of Go. It then plays games against itself, by combining this neural network with a powerful search algorithm. | |
C4783 | If you want a representative sample of a particular population, you need to ensure that:The sample source includes all the target population.The selected data collection method (online, phone, paper, in person) can reach individuals that represent that target population.More items• | |
C4784 | Here are applications of Reinforcement Learning:Robotics for industrial automation.Business strategy planning.Machine learning and data processing.It helps you to create training systems that provide custom instruction and materials according to the requirement of students.Aircraft control and robot motion control. | |
C4785 | The Artificial Neural Network receives the input signal from the external world in the form of a pattern and image in the form of a vector. Each of the input is then multiplied by its corresponding weights (these weights are the details used by the artificial neural networks to solve a certain problem). | |
C4786 | Formal definition: a nonlinear process is any stochastic process that is not linear. Realizations of time-series processes are called time series but the word is also often applied to the generating processes. | |
C4787 | There are two types of coefficients that are typically be displayed in a multiple regression table: unstandardized coefficients, and standardized coefficients. To interpret an unstandardized regression coefficient: for every metric unit change in the independent variable, the dependent variable changes by X units. | |
C4788 | To find the harmonic mean of a set of n numbers, add the reciprocals of the numbers in the set, divide the sum by n, then take the reciprocal of the result. | |
C4789 | A multilayer perceptron (MLP) is a class of feedforward artificial neural network (ANN). MLP utilizes a supervised learning technique called backpropagation for training. Its multiple layers and non-linear activation distinguish MLP from a linear perceptron. It can distinguish data that is not linearly separable. | |
C4790 | Algorithms have been criticized as a method for obscuring racial prejudices in decision-making. Because of how certain races and ethnic groups were treated in the past, data can often contain hidden biases. For example, black people are likely to receive longer sentences than white people who committed the same crime. | |
C4791 | You can think of independent and dependent variables in terms of cause and effect: an independent variable is the variable you think is the cause, while a dependent variable is the effect. In an experiment, you manipulate the independent variable and measure the outcome in the dependent variable. | |
C4792 | Lets do this step by step:Step 1: find the mean.Step 2: fin the standard deviation of the mean (using the population SD)Step 3: find the Z score.Step 4: compare to the critical Z score. From the stated hypothesis, we know that we are dealing with a 1-tailed hypothesis test. Step 4 : compare to the critical Z score. | |
C4793 | Generally, a large learning rate allows the model to learn faster, at the cost of arriving on a sub-optimal final set of weights. A smaller learning rate may allow the model to learn a more optimal or even globally optimal set of weights but may take significantly longer to train. | |
C4794 | For example, following a run of 10 heads on a flip of a fair coin (a rare, extreme event), regression to the mean states that the next run of heads will likely be less than 10, while the law of large numbers states that in the long term, this event will likely average out, and the average fraction of heads will tend to | |
C4795 | A sampling frame is a list or other device used to define a researcher's population of interest. The sampling frame defines a set of elements from which a researcher can select a sample of the target population. | |
C4796 | The Poisson distribution is a limiting case of the binomial distribution which arises when the number of trials n increases indefinitely whilst the product μ = np, which is the expected value of the number of successes from the trials, remains constant. | |
C4797 | AREA UNDER THE ROC CURVE In general, an AUC of 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), 0.7 to 0.8 is considered acceptable, 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding. | |
C4798 | 1960s | |
C4799 | In statistics, a two-tailed test is a method in which the critical area of a distribution is two-sided and tests whether a sample is greater than or less than a certain range of values. It is used in null-hypothesis testing and testing for statistical significance. |
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