_id stringlengths 2 6 | text stringlengths 3 395 | title stringclasses 1 value |
|---|---|---|
C2100 | Multiply the Grand total by the Pretest probability to get the Total with disease. Compute the Total without disease by subtraction. Multiply the Total with disease by the Sensitivity to get the number of True positives. | |
C2101 | Adam optimizer. Implements the Adam optimization algorithm. Adam is a stochastic gradient descent method that computes individual adaptive learning rates for different parameters from estimates of first- and second-order moments of the gradients. | |
C2102 | For the coin flip example, N = 2 and π = 0.5. The formula for the binomial distribution is shown below: where P(x) is the probability of x successes out of N trials, N is the number of trials, and π is the probability of success on a given trial.Number of HeadsProbability21/42 more rows | |
C2103 | dB‟ is the area of elementary strip of B –H curve shown in the figure above, Therefore, Energy consumed per cycle = volume of the right x area of hysteresis loop. The hysteresis loss per second is given by the equation[20]: Hysteresis loss, Ph= (Bmax)1.6f V joules per second (or) watts. | |
C2104 | One common method of probability sampling is random sampling, which assumes that each member of a population has an equal chance of being selected. In a quota sample, a researcher deliberately sets the proportions of levels of members chosen within the sample. | |
C2105 | When dealing with Machine Learning models, it is usually recommended that you store them somewhere. At the private sector, you oftentimes train them and store them before production, while in research and for future model tuning it is a good idea to store them locally. | |
C2106 | The main difference between Independant and Independent is that the Independant is a misspelling of independent and Independent is a Not dependent; free; not subject to control by others; not relying on others. | |
C2107 | A little bit of coding skills is enough, but it's better to have knowledge of data structures, algorithms, and OOPs concept. Some of the popular programming languages to learn machine learning in are Python, R, Java, and C++. | |
C2108 | Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing (NLP), speech recognition and machine vision. | |
C2109 | 5 Most Important Methods For Statistical Data AnalysisMean. The arithmetic mean, more commonly known as “the average,” is the sum of a list of numbers divided by the number of items on the list. Standard Deviation. Regression. Sample Size Determination. Hypothesis Testing. | |
C2110 | In artificial intelligence and computational cognitive science, "the action selection problem" is typically associated with intelligent agents and animats—artificial systems that exhibit complex behaviour in an agent environment. The term is also sometimes used in ethology or animal behavior. | |
C2111 | For the vanishing gradient problem, the further you go through the network, the lower your gradient is and the harder it is to train the weights, which has a domino effect on all of the further weights throughout the network. That was the main roadblock to using Recurrent Neural Networks. | |
C2112 | The advantage to image-based backups is that all of the information can be collected in a single pass, providing an updated bare metal restore (BMR) capability with each file-based backup. | |
C2113 | A logistic regression model allows us to establish a relationship between a binary outcome variable and a group of predictor variables. It models the logit-transformed probability as a linear relationship with the predictor variables. | |
C2114 | In some cases, the measurement scale for data is ordinal, but the variable is treated as continuous. For example, a Likert scale that contains five values - strongly agree, agree, neither agree nor disagree, disagree, and strongly disagree - is ordinal. | |
C2115 | The normal distribution can be used as an approximation to the binomial distribution, under certain circumstances, namely: If X ~ B(n, p) and if n is large and/or p is close to ½, then X is approximately N(np, npq) | |
C2116 | Stratified sampling is a probability sampling technique wherein the researcher divides the entire population into different subgroups or strata, then randomly selects the final subjects proportionally from the different strata. | |
C2117 | Convenience sampling is a type of nonprobability sampling in which people are sampled simply because they are "convenient" sources of data for researchers. In probability sampling, each element in the population has a known nonzero chance of being selected through the use of a random selection procedure. | |
C2118 | Feature Selection: Select a subset of input features from the dataset. Unsupervised: Do not use the target variable (e.g. remove redundant variables). Supervised: Use the target variable (e.g. remove irrelevant variables). Wrapper: Search for well-performing subsets of features. | |
C2119 | In machine learning, 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 derived via training. Given these hyperparameters, the training algorithm learns the parameters from the data. | |
C2120 | Stochastic Gradient Descent (SGD): Hence, in Stochastic Gradient Descent, a few samples are selected randomly instead of the whole data set for each iteration. This problem is solved by Stochastic Gradient Descent. In SGD, it uses only a single sample, i.e., a batch size of one, to perform each iteration. | |
C2121 | Precision is a metric that quantifies the number of correct positive predictions made. Precision, therefore, calculates the accuracy for the minority class. It is calculated as the ratio of correctly predicted positive examples divided by the total number of positive examples that were predicted. | |
C2122 | The task boils down to computing the distance between two face vectors. As such, appropriate distance metrics are essential for face verification accuracy. The use of cosine similarity in our method leads to an effective learning algorithm which can improve the generalization ability of any given metric. | |
C2123 | binary dependent variable | |
C2124 | Definition: Entropy is a measure of uncertainty of a random variable. The entropy of a discrete random variable X with alphabet X is H(X) = -) p(x) log p(2) DEX When the base of the logarithm is 2, entropy is measured in bits. | |
C2125 | Another strategy OTs typically recommend is something called “backward chaining." Backward chaining is working backward from the goal. For example, the goal is put on a T-shirt. Pull shirt over head. Push right arm up through right sleeve. | |
C2126 | Returning to directed acyclic graphs, the current state of the art in the Bayesian belief networks allows to efficiently deal with undirected cycles, that is, patterns which would be cycles if the arrow directions were not taken into account. | |
C2127 | Signal Processing means processing any kind of signal whether it is analog or digital in such a manner by which it can be interpreted by any kind of computer. For instance, sound wave is also a type of signal. | |
C2128 | A logistic regression estimates the mean of your response given that your data is distributed Bernoulli or is a Binomial trial. Since the mean of a Binomial trial is the probability of success, you can interpret the output from a Logistic regression (after logit transformation) as a probability of success. | |
C2129 | Model-based collaborative filtering algorithms provide item recommendation by first developing a model of user ratings. Algorithms in this category take a probabilistic approach and envision the collaborative filtering process as computing the expected value of a user prediction, given his/her ratings on other items. | |
C2130 | A: Bootstrap aggregation, or "bagging," in machine learning decreases variance through building more advanced models of complex data sets. Specifically, the bagging approach creates subsets which are often overlapping to model the data in a more involved way. | |
C2131 | The law of averages is not a mathematical principle, whereas the law of large numbers is. According to the law, the average of the results obtained from a large number of trials should be close to the expected value, and will tend to become closer as more trials are performed. | |
C2132 | In the field of machine learning and specifically the problem of statistical classification, a confusion matrix, also known as an error matrix, is a specific table layout that allows visualization of the performance of an algorithm, typically a supervised learning one (in unsupervised learning it is usually called a | |
C2133 | In the mathematical discipline of linear algebra, a matrix decomposition or matrix factorization is a factorization of a matrix into a product of matrices. There are many different matrix decompositions; each finds use among a particular class of problems. | |
C2134 | A residual sum of squares (RSS) is a statistical technique used to measure the amount of variance in a data set that is not explained by a regression model. The residual sum of squares measures the amount of error remaining between the regression function and the data set. | |
C2135 | Dive into this post for some overview of the right resources and a little bit of advice.By Pulkit Khandelwal, VIT University. Step 1 - Background Check. Step 2 - Digital Image Processing. Step 3 - Computer Vision. Step 4 - Advanced Computer Vision. Step 5 - Bring in Python and Open Source.More items | |
C2136 | Events A and B are independent if: knowing whether A occured does not change the probability of B. Mathematically, can say in two equivalent ways: P(B|A) = P(B) P(A and B) Important to distinguish independence from mutually exclusive which would say B ∩ A is empty (cannot happen). | |
C2137 | In statistics, linear regression is a linear approach to modeling the relationship between a scalar response (or dependent variable) and one or more explanatory variables (or independent variables). The case of one explanatory variable is called simple linear regression. Such models are called linear models. | |
C2138 | The two sample Kolmogorov-Smirnov test is a nonparametric test that compares the cumulative distributions of two data sets(1,2). The KS test report the maximum difference between the two cumulative distributions, and calculates a P value from that and the sample sizes. | |
C2139 | In many tests, including diagnostic medical tests, sensitivity is the extent to which true positives are not overlooked, thus false negatives are few, and specificity is the extent to which true negatives are classified as such, thus false positives are few. | |
C2140 | An ROC curve (receiver operating characteristic curve) is a graph showing the performance of a classification model at all classification thresholds. This curve plots two parameters: True Positive Rate. False Positive Rate. | |
C2141 | Backward-chaining is based on modus ponens inference rule. In backward chaining, the goal is broken into sub-goal or sub-goals to prove the facts true. It is called a goal-driven approach, as a list of goals decides which rules are selected and used. | |
C2142 | Probability mass functions (pmf) are used to describe discrete probability distributions. While probability density functions (pdf) are used to describe continuous probability distributions. | |
C2143 | A Poisson Process is a model for a series of discrete event where the average time between events is known, but the exact timing of events is random . The arrival of an event is independent of the event before (waiting time between events is memoryless). | |
C2144 | Advertisements. Searching in data-strucutre refers to the process of finding a desired element in set of items. The desired element is called "target". The set of items to be searched in, can be any data-structure like − list, array, linked-list, tree or graph. | |
C2145 | Explanation: Correlation is the process of studying the cause and effect relationship that exists between two variables. Correlation coefficient is the measure of the correlation that exists between two variables. | |
C2146 | Z-tests are statistical calculations that can be used to compare population means to a sample's. T-tests are calculations used to test a hypothesis, but they are most useful when we need to determine if there is a statistically significant difference between two independent sample groups. | |
C2147 | The significance level for a given hypothesis test is a value for which a P-value less than or equal to is considered statistically significant. Typical values for are 0.1, 0.05, and 0.01. These values correspond to the probability of observing such an extreme value by chance. | |
C2148 | In the chapter on Human Development Indicators, there should be a table that includes the Gini coefficient. For example, in the 2004 edition, they are in table number 14. See also the “Get Indicators” portion of their web site, where you can download an Excel table with the Gini index. | |
C2149 | The Central limit Theorem states that when sample size tends to infinity, the sample mean will be normally distributed. The Law of Large Number states that when sample size tends to infinity, the sample mean equals to population mean. | |
C2150 | The following types of inferential statistics are extensively used and relatively easy to interpret: One sample test of difference/One sample hypothesis test. Confidence Interval. Contingency Tables and Chi Square Statistic. | |
C2151 | Chi-squared test | |
C2152 | Modes, medians, and frequencies are the appropriate statistical tools to use. If you have designed a series of questions that when combined measure a particular trait, you have created a Likert scale. Use means and standard deviations to describe the scale. | |
C2153 | The confidence interval (CI) is a range of values that's likely to include a population value with a certain degree of confidence. It is often expressed a % whereby a population means lies between an upper and lower interval. | |
C2154 | The average value becomes more and more precise as the number of measurements N increases. Although the uncertainty of any single measurement is always ��, the uncertainty in the mean ��avg becomes smaller (by a factor of 1/ N) as more measurements are made. You measure the length of an object five times. | |
C2155 | Cluster analysis, or clustering, is an unsupervised machine learning task. It involves automatically discovering natural grouping in data. Unlike supervised learning (like predictive modeling), clustering algorithms only interpret the input data and find natural groups or clusters in feature space. | |
C2156 | Ensemble learning is usually used to average the predictions of different models to get a better prediction. Ensemble methods is like using the predictions of small expert models in different parts of the input space. | |
C2157 | Deep NN is just a deep neural network, with a lot of layers. It can be CNN, or just a plain multilayer perceptron. CNN, or convolutional neural network, is a neural network using convolution layer and pooling layer. | |
C2158 | Backward chaining is a type of AI program that starts with a defined end point (or goal) and works backward to figure out the best way to get there. For example, if a person wants to save $1 million for retirement, backward chaining can help them figure out how much they need to save each month to get there. | |
C2159 | AI Strategy is a road plan for the adoption and implementation of artificial intelligence, machine learning, or deep learning technologies within your organization. An AI Strategy defines your AI priorities, goals, milestones, mission and vision. AI Strategies are being used in corporations around the world. | |
C2160 | A Second Order Low Pass Filter is to be design around a non-inverting op-amp with equal resistor and capacitor values in its cut-off frequency determining circuit. If the filters characteristics are given as: Q = 5, and ƒc = 159Hz, design a suitable low pass filter and draw its frequency response. | |
C2161 | The accuracy is calculated by taking the percentage of correct predictions over the total number of examples. Correct prediction means the examples where the value of the prediction attribute is equal to the value of label attribute. | |
C2162 | A continuous random variable can take on an infinite number of values. The probability that it will equal a specific value (such as a) is always zero. | |
C2163 | If we multiply the variances by then the sample variances would also match the population variance. In statistics, we take a sample of a population and say that the sample mean and sample variance are the same as the population mean and variance. | |
C2164 | OLS cannot be used because the regression function is not a linear function of the regression coefficients (the coefficients appear inside the nonlinear functions Φ or Λ). | |
C2165 | The Matrix represents a system of control that operates completely in the mind. As a complex, machine-driven program, it appropriates any personal, political, or ideological leanings and renders them wholly false. It allows illusions but no action. | |
C2166 | Dimensionality reduction refers to techniques that reduce the number of input variables in a dataset. Large numbers of input features can cause poor performance for machine learning algorithms. Dimensionality reduction is a general field of study concerned with reducing the number of input features. | |
C2167 | There are four types of artificial intelligence: reactive machines, limited memory, theory of mind and self-awareness. | |
C2168 | Cross tabulationCross tabulations require that the two data columns be adjacent. You can drag columns by selecting them, and moving the cursor so it's immediately between two columns. Once you have the columns adjacent, select both of them including the variable names all the way to the bottom. | |
C2169 | Neural networks are sets of algorithms intended to recognize patterns and interpret data through clustering or labeling. In other words, neural networks are algorithms. A training algorithm is the method you use to execute the neural network's learning process. | |
C2170 | Share on. Statistics Definitions > Paired data is where natural matching or coupling is possible. Generally this would be data sets where every data point in one independent sample would be paired—uniquely—to a data point in another independent sample. | |
C2171 | The difference between forward and backward chaining is: Backward chaining starts with a goal and then searches back through inference rules to find the facts that support the goal. Forward chaining starts with facts and searches forward through the rules to find a desired goal. | |
C2172 | How to Test HypothesesState the hypotheses. Every hypothesis test requires the analyst to state a null hypothesis and an alternative hypothesis. Formulate an analysis plan. The analysis plan describes how to use sample data to accept or reject the null hypothesis. Analyze sample data. Interpret the results. | |
C2173 | A generative adversarial network (GAN) is a class of machine learning frameworks designed by Ian Goodfellow and his colleagues in 2014. Two neural networks contest with each other in a game (in the form of a zero-sum game, where one agent's gain is another agent's loss). | |
C2174 | An activation function is a function used in artificial neural networks which outputs a small value for small inputs, and a larger value if its inputs exceed a threshold. Activation functions are useful because they add non-linearities into neural networks, allowing the neural networks to learn powerful operations. | |
C2175 | R-squared is a statistical measure of how close the data are to the fitted regression line. It is also known as the coefficient of determination, or the coefficient of multiple determination for multiple regression. 100% indicates that the model explains all the variability of the response data around its mean. | |
C2176 | The parameters of a neural network are typically the weights of the connections. In this case, these parameters are learned during the training stage. So, the algorithm itself (and the input data) tunes these parameters. The hyper parameters are typically the learning rate, the batch size or the number of epochs. | |
C2177 | Latent semantic indexing (LSI) is a concept used by search engines to discover how a term and content work together to mean the same thing, even if they do not share keywords or synonyms. Basically, though, you often need specific keywords on your pages to boost your website traffic. | |
C2178 | The Bayes theorem describes the probability of an event based on the prior knowledge of the conditions that might be related to the event. If we know the conditional probability , we can use the bayes rule to find out the reverse probabilities . | |
C2179 | We can use classification performance metrics such as Log-Loss, Accuracy, AUC(Area under Curve) etc. Another example of metric for evaluation of machine learning algorithms is precision, recall, which can be used for sorting algorithms primarily used by search engines. | |
C2180 | Calculate output size of ConvolutionOutput height = (Input height + padding height top + padding height bottom - kernel height) / (stride height) + 1.Output width = (Output width + padding width right + padding width left - kernel width) / (stride width) + 1. | |
C2181 | Cluster sampling refers to a type of sampling method . With cluster sampling, the researcher divides the population into separate groups, called clusters. Then, a simple random sample of clusters is selected from the population. The researcher conducts his analysis on data from the sampled clusters. | |
C2182 | The Poisson distribution is used to describe the distribution of rare events in a large population. For example, at any particular time, there is a certain probability that a particular cell within a large population of cells will acquire a mutation. | |
C2183 | The two-sample t-test is a method used to test whether the unknown population means of two groups are equal or not. | |
C2184 | 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). Well, that wasn't very interesting or exciting. | |
C2185 | The reason n-1 is used is because that is the number of degrees of freedom in the sample. The sum of each value in a sample minus the mean must equal 0, so if you know what all the values except one are, you can calculate the value of the final one. | |
C2186 | Statistical SignificanceUsually, statistical significance is determined by calculating the probability of error (p value) by the t ratio.The difference between two groups (such as an experiment vs. control group) is judged to be statistically significant when p = 0.05 or less. | |
C2187 | Bootstrapping assigns measures of accuracy (bias, variance, confidence intervals, prediction error, etc.) to sample estimates. This technique allows estimation of the sampling distribution of almost any statistic using random sampling methods. | |
C2188 | Matrix decomposition refers to the transformation of a given matrix into a product of matrices. They are used to implement efficient matrix algorithms. Decomposing a matrix breaks it up into two matrix factors. This can be helpful when solving equations of the form Ax=b for x when multiple b vectors are to be used. | |
C2189 | Many time series show periodic behavior. This periodic behavior can be very complex. Spectral analysis is a technique that allows us to discover underlying periodicities. To perform spectral analysis, we first must transform data from time domain to frequency domain. | |
C2190 | A relative frequency distribution shows the proportion of the total number of observations associated with each value or class of values and is related to a probability distribution, which is extensively used in statistics. | |
C2191 | Statistical Decision for Hypothesis Testing In Hypothesis testing, if the significance value of the test is greater than the predetermined significance level, then we accept the null hypothesis. If the significance value is less than the predetermined value, then we should reject the null hypothesis. | |
C2192 | 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. | |
C2193 | Normal distribution, also known as the Gaussian distribution, is a probability distribution that is symmetric about the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. In graph form, normal distribution will appear as a bell curve. | |
C2194 | Consider the function f(x) = |x| on [−1,1]. • The Mean Value Theorem does not apply because the derivative is not defined at x = 0. | |
C2195 | The logistic function is the inverse of the natural logit function and so can be used to convert the logarithm of odds into a probability. In mathematical notation the logistic function is sometimes written as expit in the same form as logit. | |
C2196 | The exponential distribution predicts the wait time until the *very first* event. The gamma distribution, on the other hand, predicts the wait time until the *k-th* event occurs. | |
C2197 | In a positively skewed distribution, the mean is usually greater than the median because the few high scores tend to shift the mean to the right. In a positively skewed distribution, the mode is always less than the mean and median. | |
C2198 | Deep learning is a type of machine learning in which a model learns to perform classification tasks directly from images, text or sound. Deep learning is usually implemented using neural network architecture. The term deep refers to the number of layers in the network—the more the layers, the deeper the network. | |
C2199 | The computation of a single layer perceptron is performed over the calculation of sum of the input vector each with the value multiplied by corresponding element of vector of the weights. The value which is displayed in the output will be the input of an activation function. |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.