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C7100 | The exponential distribution is a continuous probability distribution used to model the time we need to wait before a given event occurs. It is the continuous counterpart of the geometric distribution, which is instead discrete. Sometimes it is also called negative exponential distribution. | |
C7101 | The probability of making a type I error is represented by your alpha level (α), which is the p-value below which you reject the null hypothesis. A p-value of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. | |
C7102 | A histogram is a graphical display of data using bars of different heights. In a histogram, each bar groups numbers into ranges. Taller bars show that more data falls in that range. A histogram displays the shape and spread of continuous sample data. | |
C7103 | PSD is typically measured in units of Vrms2 /Hz or Vrms/rt Hz , where "rt Hz" means "square root Hertz". Alternatively, PSD can be expressed in units of dBm/Hz. On a spectrum analyzer such as the PSA, ESA, 856XE/EC or 859XE, power spectral density can be measured with the noise marker. | |
C7104 | The coefficient of determination is a statistical measurement that examines how differences in one variable can be explained by the difference in a second variable, when predicting the outcome of a given event. | |
C7105 | The distribution of sample statistics is called sampling distribution. Next a new sample of sixteen is taken, and the mean is again computed. If this process were repeated an infinite number of times, the distribution of the now infinite number of sample means would be called the sampling distribution of the mean. | |
C7106 | 0:382:54Suggested clip · 77 secondsClass Boundaries - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7107 | We have now found a test for determining whether a given set of vectors is linearly independent: A set of n vectors of length n is linearly independent if the matrix with these vectors as columns has a non-zero determinant. The set is of course dependent if the determinant is zero. | |
C7108 | In 2019, according to the Gini coefficient, household income distribution in the United States was 0.48. This figure was at 0.43 in 1990, which indicates an increase in income inequality in the U.S. over the past 30 years. | |
C7109 | VARIABLE: Characteristic which varies between independent subjects. CONTINUOUS (SCALE) VARIABLES: Measurements on a proper scale such as age, height etc. INDEPENDENT VARIABLE: The variable we think has an effect on the dependent variable. | |
C7110 | The finite population correction (fpc) factor is used to adjust a variance estimate for an estimated mean or total, so that this variance only applies to the portion of the population that is not in the sample. | |
C7111 | Covariance measures the total variation of two random variables from their expected values. Obtain the data.Calculate the mean (average) prices for each asset.For each security, find the difference between each value and mean price.Multiply the results obtained in the previous step.More items | |
C7112 | Dropout causes your network to keep only some portion of neurons/weights on each iteration. Sometimes those neurons do not fit the current minibatch well, and this may cause large fluctuations. | |
C7113 | The optimal K value usually found is the square root of N, where N is the total number of samples. Use an error plot or accuracy plot to find the most favorable K value. KNN performs well with multi-label classes, but you must be aware of the outliers. | |
C7114 | A Formal Definition for Concept Learning: Inferring a boolean-valued function from training examples of its input and output. • An example for concept-learning is the learning of bird-concept from the given examples of birds (positive examples) and non-birds (negative examples). • | |
C7115 | The expected value (EV) is an anticipated value for an investment at some point in the future. In statistics and probability analysis, the expected value is calculated by multiplying each of the possible outcomes by the likelihood each outcome will occur and then summing all of those values. | |
C7116 | Acts are the actions being considered by the agent -in the example elow, taking the raincoat or not; events are occurrences taking place outside the control of the agent (rain or lack thereof); outcomes are the result of the occurrence (or lack of it) of acts and events (staying dry or not; being burdened by the | |
C7117 | RL is an increasingly popular technique for organizations that deal regularly with large complex problem spaces. Because RL models learn by a continuous process of receiving rewards and punishments on every action taken, it is able to train systems to respond to unforeseen environments . | |
C7118 | However, RNNs suffer from the problem of vanishing gradients, which hampers learning of long data sequences. The gradients carry information used in the RNN parameter update and when the gradient becomes smaller and smaller, the parameter updates become insignificant which means no real learning is done. | |
C7119 | Backward chaining is the logical process of inferring unknown truths from known conclusions by moving backward from a solution to determine the initial conditions and rules. Backward chaining is often applied in artificial intelligence (AI) and may be used along with its counterpart, forward chaining. | |
C7120 | In statistics, a Bayesian is someone who tries to determine the probability that a theory is true given the observed data. This is in contrast to classical statisticians, who work with the probability of observing certain data assuming a theory. | |
C7121 | When we calculate Z, we will get a value. If this value falls into the middle part, then we cannot reject the null. If it falls outside, in the shaded region, then we reject the null hypothesis. That is why the shaded part is called: rejection region, as you can see below. | |
C7122 | In Reinforcement Learning, this type of decision is called exploitation when you keep doing what you were doing, and exploration when you try something new. In Reinforcement Learning on the other hand, it is not possible to do that, but there are some techniques that will help figuring out the best strategy. | |
C7123 | Linear programming, mathematical modeling technique in which a linear function is maximized or minimized when subjected to various constraints. This technique has been useful for guiding quantitative decisions in business planning, in industrial engineering, and—to a lesser extent—in the social and physical sciences. | |
C7124 | No. You can have dependent events that are not mutually exclusive. | |
C7125 | Look up the normal distribution in a statistics table. Statistics tables can be found online or in statistics textbooks. Find the value for the intersection of the correct degrees of freedom and alpha. If this value is less than or equal to the chi-square value, the data is statistically significant. | |
C7126 | In statistics, Markov chain Monte Carlo (MCMC) methods comprise a class of algorithms for sampling from a probability distribution. By constructing a Markov chain that has the desired distribution as its equilibrium distribution, one can obtain a sample of the desired distribution by recording states from the chain. | |
C7127 | Graphs that are appropriate for bivariate analysis depend on the type of variable. For two continuous variables, a scatterplot is a common graph. When one variable is categorical and the other continuous, a box plot is common and when both are categorical a mosaic plot is common. | |
C7128 | Pierre-Simon Laplace | |
C7129 | The potential solutions include the following:Remove some of the highly correlated independent variables.Linearly combine the independent variables, such as adding them together.Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression. | |
C7130 | For more tips, read 10 Best Practices for Effective Dashboards.Choose the right charts and graphs for the job. Use predictable patterns for layouts. Tell data stories quickly with clear color cues. Incorporate contextual clues with shapes and designs. Strategically use size to visualize values.More items | |
C7131 | A Markov network or MRF is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed and acyclic , whereas Markov networks are undirected and may be cyclic. The underlying graph of a Markov random field may be finite or infinite. | |
C7132 | Decision tree learning is one of the predictive modelling approaches used in statistics, data mining and machine learning. It uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). | |
C7133 | The term “multivariate statistics” is appropriately used to include all statistics where there are more than two variables simultaneously analyzed. You are already familiar with bivariate statistics such as the Pearson product moment correlation coefficient and the independent groups t-test. | |
C7134 | It began with the “heartless” Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. | |
C7135 | The Poisson probability density function lets you obtain the probability of an event occurring within a given time or space interval exactly x times if on average the event occurs λ times within that interval. f ( x | λ ) = λ x x ! e − λ ; x = 0 , 1 , 2 , … , ∞ . | |
C7136 | When detecting bias, computer programmers normally examine the set of outputs that the algorithm produces to check for anomalous results. Comparing outcomes for different groups can be a useful first step. This could even be done through simulations. | |
C7137 | Systematic bias is sampling error that stems from the way in which the research is conducted and can therefore be controled by the researcher. There are three types: Response bias: A biased view arises, because the answers that are given are not in accordance with the truth. | |
C7138 | Causation is the relationship between cause and effect. So, when a cause results in an effect, that's a causation. When we say that correlation does not imply cause, we mean that just because you can see a connection or a mutual relationship between two variables, it doesn't necessarily mean that one causes the other. | |
C7139 | Important classes of stochastic processes are Markov chains and Markov processes. A Markov chain is a discrete-time process for which the future behaviour, given the past and the present, only depends on the present and not on the past. A Markov process is the continuous-time version of a Markov chain. | |
C7140 | Decision Tree algorithm has become one of the most used machine learning algorithm both in competitions like Kaggle as well as in business environment. Decision Tree can be used both in classification and regression problem. | |
C7141 | Given that the range can easily be computed with information on the maximum and minimum value of the data set, users requiring only a rough indication of the data may prefer to use this indicator over more sophisticated measures of spread, like the standard deviation. | |
C7142 | An estimator of a given parameter is said to be consistent if it converges in probability to the true value of the parameter as the sample size tends to infinity. | |
C7143 | Bootstrapping is building a company from the ground up with nothing but personal savings, and with luck, the cash coming in from the first sales. The term is also used as a noun: A bootstrap is a business an entrepreneur with little or no outside cash or other support launches. | |
C7144 | Ap: NIST SP 800-22rev1a (dated April 2010), A Statistical Test Suite for the Validation of Random Number Generators and Pseudo Random Number Generators for Cryptographic Applications, that describes the test suite. Download the NIST Statistical Test Suite. | |
C7145 | If you restrict yourself to linear kernels, both SVMs and LR will give almost identical performance and in some cases, LR will beat SVM. If we compare logistic regression with SVMs with non-linear kernels, then SVMs beat LRs hands down. | |
C7146 | Depending on the alternative hypothesis operator, greater than operator will be a right tailed test, less than operator is a left tailed test, and not equal operator is a two tailed test. | |
C7147 | Like random forests, gradient boosting is a set of decision trees. The two main differences are: Combining results: random forests combine results at the end of the process (by averaging or "majority rules") while gradient boosting combines results along the way. | |
C7148 | Face detection is a broader term than face recognition. Face detection just means that a system is able to identify that there is a human face present in an image or video. Face recognition can confirm identity. It is therefore used to control access to sensitive areas. | |
C7149 | Mathematically speaking, a decision tree has low bias and high variance. Averaging the result of many decision trees reduces the variance while maintaining that low bias. Combining trees is known as an 'ensemble method'. | |
C7150 | Sentiment analysis (or opinion mining) uses natural language processing and machine learning to interpret and classify emotions in subjective data. Sentiment analysis is often used in business to detect sentiment in social data, gauge brand reputation, and understand customers. | |
C7151 | 1. A Simple Way of Solving an Object Detection Task (using Deep Learning)First, we take an image as input:Then we divide the image into various regions:We will then consider each region as a separate image.Pass all these regions (images) to the CNN and classify them into various classes.More items• | |
C7152 | Nonparametric tests are sometimes called distribution-free tests because they are based on fewer assumptions (e.g., they do not assume that the outcome is approximately normally distributed). There are several statistical tests that can be used to assess whether data are likely from a normal distribution. | |
C7153 | A z-test is a statistical test used to determine whether two population means are different when the variances are known and the sample size is large. A z-statistic, or z-score, is a number representing how many standard deviations above or below the mean population a score derived from a z-test is. | |
C7154 | Hyperplanes are decision boundaries that help classify the data points. Data points falling on either side of the hyperplane can be attributed to different classes. Also, the dimension of the hyperplane depends upon the number of features. | |
C7155 | They are also employed for jobs which are too dirty, dangerous or dull to be suitable for humans. Robots are widely used in manufacturing, assembly and packing, transport, earth and space exploration, surgery, weaponry, laboratory research, and mass production of consumer and industrial goods. | |
C7156 | In mathematics, the membership function of a fuzzy set is a generalization of the indicator function for classical sets. In fuzzy logic, it represents the degree of truth as an extension of valuation. | |
C7157 | Selection bias can result when the selection of subjects into a study or their likelihood of being retained in the study leads to a result that is different from what you would have gotten if you had enrolled the entire target population. | |
C7158 | In contrast, quota sampling in qualitative research is a specific technique for selecting a sample that has been defined using a purposive sampling strategy to define the categories of data sources that are eligible for a study. | |
C7159 | For a prior distribution expressed as beta(θ|a,b), the prior mean of θ is a/(a + b). Suppose we observe z heads in N flips, which is a proportion of z/N heads in the data. The posterior mean is (z + a)/[(z + a) + (N ‒ z + b)] = (z + a)/(N + a + b). | |
C7160 | Theorem 1.2 Suppose that ψ is a simple random point process that has both stationary and independent increments. Then in fact, ψ is a Poisson process. Thus the Poisson process is the only simple point process with stationary and independent increments. | |
C7161 | Basic rules for logarithmsRule or special caseFormulaProductln(xy)=ln(x)+ln(y)Quotientln(x/y)=ln(x)−ln(y)Log of powerln(xy)=yln(x)Log of eln(e)=12 more rows | |
C7162 | Statistical Validity is the extent to which the conclusions drawn from a statistical test are accurate and reliable. To achieve statistical validity, researchers must have an adequate sample size and pick the right statistical test to analyze the data. | |
C7163 | Properties of F-DistributionThe F-distribution is positively skewed and with the increase in the degrees of freedom ν1 and ν2, its skewness decreases.The value of the F-distribution is always positive, or zero since the variances are the square of the deviations and hence cannot assume negative values.More items | |
C7164 | The basic problem that the attention mechanism solves is that it allows the network to refer back to the input sequence, instead of forcing it to encode all information into one fixed-length vector. | |
C7165 | The probability distribution associated with a random categorical variable is called a categorical distribution. Categorical data is the statistical data type consisting of categorical variables or of data that has been converted into that form, for example as grouped data. | |
C7166 | The value of a dependent variable depends on an independent variable, so a variable cannot be both independent and dependent at the same time. It must be either the cause or the effect, not both! | |
C7167 | Increasing the temperature will increase the entropy. Changes in volume will lead to changes in entropy. The larger the volume the more ways there are to distribute the molecules in that volume; the more ways there are to distribute the molecules (energy), the higher the entropy. | |
C7168 | Training artificial intelligence (AI) without datasets derived from human experts has significant implications for the development of AI with superhuman skills because expert data is "often expensive, unreliable or simply unavailable." Demis Hassabis, the co-founder and CEO of DeepMind, said that AlphaGo Zero was so | |
C7169 | 2.1 Steps of Bayesian Data Analysis Choose a statistical model for the data in relation to the research questions. The model should have good theoretical justification and have parameters that are meaningful for the research questions. Obtain the posterior distributions for the model parameters. | |
C7170 | A one-tailed test is a statistical test in which the critical area of a distribution is one-sided so that it is either greater than or less than a certain value, but not both. A one-tailed test is also known as a directional hypothesis or directional test. | |
C7171 | Gradient Descent is the process of minimizing a function by following the gradients of the cost function. This involves knowing the form of the cost as well as the derivative so that from a given point you know the gradient and can move in that direction, e.g. downhill towards the minimum value. | |
C7172 | Collaborative Filtering Advantages & DisadvantagesNo domain knowledge necessary.Serendipity.Great starting point.Cannot handle fresh items.Hard to include side features for query/item. | |
C7173 | Normal Approximation to the Binomialn is your sample size,p is your given probability.q is just 1 – p. For example, let's say your probability p is . You would find q by subtracting this probability from 1: q = 1 – . 6 = . | |
C7174 | In order for the system to function, it's necessary to implement three steps. First, it must detect a face. Then, it must recognize that face nearly instantaneously. Finally, it must take whatever further action is required, such as allowing access for an approved user. | |
C7175 | Simple regression analysis uses a single x variable for each dependent “y” variable. For example: (x1, Y1). Multiple regression uses multiple “x” variables for each independent variable: (x1)1, (x2)1, (x3)1, Y1). | |
C7176 | There are mainly four ways of knowledge representation which are given as follows: Logical Representation. Semantic Network Representation. Frame Representation. Production Rules. | |
C7177 | Serial correlation is the relationship between a variable and a lagged version of itself over various time intervals. Repeating patterns often show serial correlation when the level of a variable affects its future level. Serial correlation is also known as autocorrelation or lagged correlation. | |
C7178 | Likelihood ratios range from zero to infinity. The higher the value, the more likely the patient has the condition. As an example, let's say a positive test result has an LR of 9.2. This result is 9.2 times more likely to happen in a patient with the condition than it would in a patient without the condition. | |
C7179 | In probability theory and statistics, the hypergeometric distribution is a discrete probability distribution that describes the probability of successes (random draws for which the object drawn has a specified feature) in draws, without replacement, from a finite population of size that contains exactly objects with | |
C7180 | There are two main methods for tackling a multi-label classification problem: problem transformation methods and algorithm adaptation methods. Problem transformation methods transform the multi-label problem into a set of binary classification problems, which can then be handled using single-class classifiers. | |
C7181 | A continuous random variable Z is said to be a standard normal (standard Gaussian) random variable, shown as Z∼N(0,1), if its PDF is given by fZ(z)=1√2πexp{−z22},for all z∈R. | |
C7182 | The joint behavior of two random variables X and Y is determined by the. joint cumulative distribution function (cdf):(1.1) FXY (x, y) = P(X ≤ x, Y ≤ y),where X and Y are continuous or discrete. For example, the probability. P(x1 ≤ X ≤ x2,y1 ≤ Y ≤ y2) = F(x2,y2) − F(x2,y1) − F(x1,y2) + F(x1,y1). | |
C7183 | An open source software library to carry out numerical computation using data flow graphs, the base language for TensorFlow is C++ or Python, whereas Theano is completely Python based library that allows user to define, optimize and evaluate mathematical expressions evolving multi-dimensional arrays efficiently, as per | |
C7184 | 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. | |
C7185 | Sometimes we are given a chart showing frequencies of certain groups instead of the actual values. If we multiply each midpoint by its frequency, and then divide by the total number of values in the frequency distribution, we have an estimate of the mean. | |
C7186 | A random variable is discrete if it has a finite number of possible outcomes, or a countable number (i.e. the integers are infinite, but are able to be counted). For example, the number of heads you get when flip a coin 100 times is discrete, since it can only be a whole number between 0 and 100. | |
C7187 | Multiclass classification with logistic regression can be done either through the one-vs-rest scheme in which for each class a binary classification problem of data belonging or not to that class is done, or changing the loss function to cross- entropy loss. By default, multi_class is set to 'ovr'. | |
C7188 | Answer. True is the answer of Restricted Boltzmann Machine expect data to be labeled for Training as because there are two process for training one which is called as pre-training and training. In pre-training one don't need labeled data. | |
C7189 | Attention is proposed as a method to both align and translate. Alignment is the problem in machine translation that identifies which parts of the input sequence are relevant to each word in the output, whereas translation is the process of using the relevant information to select the appropriate output. | |
C7190 | Generally, you're evidently not an AI, if we are talking about the computers and algorithms and codes. You cannot prove this topic unless you definitely define what is artificial intelligence and what you are. Generally, you're evidently not an AI, if we are talking about the computers and algorithms and codes. | |
C7191 | A mixed model, mixed-effects model or mixed error-component model is a statistical model containing both fixed effects and random effects. Because of their advantage in dealing with missing values, mixed effects models are often preferred over more traditional approaches such as repeated measures ANOVA. | |
C7192 | Fuelled by successes in Computer Go, Monte Carlo tree search (MCTS) has achieved widespread adoption within the games community. Its links to traditional reinforcement learning (RL) methods have been outlined in the past; however, the use of RL techniques within tree search has not been thoroughly studied yet. | |
C7193 | In a nutshell, the goal of Bayesian inference is to maintain a full posterior probability distribution over a set of random variables. Sampling algorithms based on Monte Carlo Markov Chain (MCMC) techniques are one possible way to go about inference in such models. | |
C7194 | If your goal is to target those with buying intent, you can do so by layering an in-market audience on top of your custom affinity audience. This will signal to Google that you want to show ads to people inside the affinity audience you've created who also have shown intent to buy in their search history. | |
C7195 | Standard deviation measures the spread of a data distribution. It measures the typical distance between each data point and the mean. If the data is a sample from a larger population, we divide by one fewer than the number of data points in the sample, n − 1 n-1 n−1 . | |
C7196 | Logistic Regression is a Machine Learning algorithm which is used for the classification problems, it is a predictive analysis algorithm and based on the concept of probability. | |
C7197 | One of the best is using recurrent neural networks for automatic feature extraction. You can use wors2vec as raw inputs to the network. A step further is to use LSTM nodes in RNNs for modeling long term dependencies. | |
C7198 | A single object of the world from which a model will be learned, or on which a model will be used (e.g., for prediction). In most machine learning work, instances are described by feature vectors; some work uses more complex representations (e.g., containing relations between instances or between parts of instances). | |
C7199 | First you will learn user-user collaborative filtering, an algorithm that identifies other people with similar tastes to a target user and combines their ratings to make recommendations for that user. |
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