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C2200 | An embedding is a mapping of a discrete — categorical — variable to a vector of continuous numbers. In the context of neural networks, embeddings are low-dimensional, learned continuous vector representations of discrete variables. As input to a machine learning model for a supervised task. | |
C2201 | Absolute standardized differences for baseline covariates comparing treated to untreated subjects in the original and the matched sample. Thus, when the standardized difference is equal to 0.10, the percentage of non-overlap between the distributions of the continuous covariate in the two groups is 7.7 per cent. | |
C2202 | A second type of quantitative variable is called a continuous variable . This is a variable where the scale is continuous and not made up of discrete steps. For example, if playing a game of trivia, the length of time it takes a player to give an answer might be represented by a continuous variable. | |
C2203 | volves the estimation of some components for some dates by interpola- tion. between values ("benchmarks") for earlier and later dates. This is often done by using a related series known for all relevant dates. In practice, the bulk of such interpolation uses only a single related. | |
C2204 | In deep learning, transfer learning is a technique whereby a neural network model is first trained on a problem similar to the problem that is being solved. One or more layers from the trained model are then used in a new model trained on the problem of interest. | |
C2205 | 7 Techniques to Handle Imbalanced DataUse the right evaluation metrics. Resample the training set. Use K-fold Cross-Validation in the right way. Ensemble different resampled datasets. Resample with different ratios. Cluster the abundant class. Design your own models. | |
C2206 | The histogram of oriented gradients (HOG) is a feature descriptor used in computer vision and image processing for the purpose of object detection. The technique counts occurrences of gradient orientation in localized portions of an image. | |
C2207 | Disadvantages of Sampling Since choice of sampling method is a judgmental task, there exist chances of biasness as per the mindset of the person who chooses it. Improper selection of sampling techniques may cause the whole process to defunct. Selection of proper size of samples is a difficult job. | |
C2208 | In regression with multiple independent variables, the coefficient tells you how much the dependent variable is expected to increase when that independent variable increases by one, holding all the other independent variables constant. Remember to keep in mind the units which your variables are measured in. | |
C2209 | Regression is a statistical method used in finance, investing, and other disciplines that attempts to determine the strength and character of the relationship between one dependent variable (usually denoted by Y) and a series of other variables (known as independent variables). | |
C2210 | Text analytics is the automated process of translating large volumes of unstructured text into quantitative data to uncover insights, trends, and patterns. Combined with data visualization tools, this technique enables companies to understand the story behind the numbers and make better decisions. | |
C2211 | Linear Regression Analysis consists of more than just fitting a linear line through a cloud of data points. It consists of 3 stages – (1) analyzing the correlation and directionality of the data, (2) estimating the model, i.e., fitting the line, and (3) evaluating the validity and usefulness of the model. | |
C2212 | Q-learning is a model-free reinforcement learning algorithm to learn quality of actions telling an agent what action to take under what circumstances. "Q" names the function that the algorithm computes with the maximum expected rewards for an action taken in a given state. | |
C2213 | A Z-score is a numerical measurement that describes a value's relationship to the mean of a group of values. Z-score is measured in terms of standard deviations from the mean. If a Z-score is 0, it indicates that the data point's score is identical to the mean score. | |
C2214 | A Multi Layer Perceptron (MLP) contains one or more hidden layers (apart from one input and one output layer). While a single layer perceptron can only learn linear functions, a multi layer perceptron can also learn non – linear functions. Figure 4 shows a multi layer perceptron with a single hidden layer. | |
C2215 | Intel® Movidius™ VPUs enable demanding computer vision and edge AI workloads with efficiency. VPU technology enables intelligent cameras, edge servers and AI appliances with deep neural network and computer vision based applications in areas such as visual retail, security and safety, and industrial automation. | |
C2216 | Vectors have many real-life applications, including situations involving force or velocity. For example, consider the forces acting on a boat crossing a river. The boat's motor generates a force in one direction, and the current of the river generates a force in another direction. Both forces are vectors. | |
C2217 | A Poisson distribution is a tool that helps to predict the probability of certain events from happening when you know how often the event has occurred. It gives us the probability of a given number of events happening in a fixed interval of time. λ (also written as μ) is the expected number of event occurrences. | |
C2218 | They provide a natural way to handle missing data, they allow combination of data with domain knowledge, they facilitate learning about causal relationships between variables, they provide a method for avoiding overfitting of data (Heckerman, 1995), they can show good prediction accuracy even with rather small sample | |
C2219 | If the study is based on a very large sample size, relationships found to be statistically significant may not have much practical significance. Almost any null hypothesis can be rejected if the sample size is large enough. | |
C2220 | The hazard rate refers to the rate of death for an item of a given age (x). It is part of a larger equation called the hazard function, which analyzes the likelihood that an item will survive to a certain point in time based on its survival to an earlier time (t). | |
C2221 | The test statistic is a z-score (z) defined by the following equation. z=(p−P)σ where P is the hypothesized value of population proportion in the null hypothesis, p is the sample proportion, and σ is the standard deviation of the sampling distribution. | |
C2222 | Examples of continuous variables are body mass, height, blood pressure and cholesterol. A discrete quantitative variable is one that can only take specific numeric values (rather than any value in an interval), but those numeric values have a clear quantitative interpretation. | |
C2223 | Logistic regression is easier to implement, interpret, and very efficient to train. If the number of observations is lesser than the number of features, Logistic Regression should not be used, otherwise, it may lead to overfitting. It makes no assumptions about distributions of classes in feature space. | |
C2224 | Bias is a tendency to lean in a certain direction, either in favor of or against a particular thing. To be truly biased means to lack a neutral viewpoint on a particular topic. If you're biased toward something, then you lean favorably toward it; you tend to think positively of it. | |
C2225 | In probability theory, the multi-armed bandit problem (sometimes called the K- or N-armed bandit problem) is a problem in which a fixed limited set of resources must be allocated between competing (alternative) choices in a way that maximizes their expected gain, when each choice's properties are only partially known | |
C2226 | Fisher's exact test is a statistical test used to determine if there are nonrandom associations between two categorical variables. . For each one, calculate the associated conditional probability using (2), where the sum of these probabilities must be 1. | |
C2227 | The null hypothesis is the one to be tested and the alternative is everything else. In our example, The null hypothesis would be: The mean data scientist salary is 113,000 dollars. While the alternative: The mean data scientist salary is not 113,000 dollars. | |
C2228 | A class of unsupervised models from Deep Learning called Autoencoders have been used as unsupervised models for time-series data. A class of unsupervised models from Deep Learning called Autoencoders have been used as unsupervised models for time-series data. | |
C2229 | Neural network in a nutshell This is done using gradient descent (aka backpropagation), which by definition comprises two steps: calculating gradients of the loss/error function, then updating existing parameters in response to the gradients, which is how the descent is done. | |
C2230 | 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• | |
C2231 | Interaction effects occur when the effect of one variable depends on the value of another variable. In this manner, analysts use models to assess the relationship between each independent variable and the dependent variable. This kind of an effect is called a main effect. | |
C2232 | 0:007:47Suggested clip · 116 seconds[Proof] Sequence is divergent - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C2233 | A simple random sample is used to represent the entire data population and. randomly selects individuals from the population without any other consideration. A stratified random sample, on the other hand, first divides the population into smaller groups, or strata, based on shared characteristics. | |
C2234 | In short, linear regression is one of the mathematical models to describe the (linear) relationship between input and output. Least squares, on the other hand, is a method to metric and estimate models, in which the optimal parameters have been found. | |
C2235 | Also known as a parallel boxplot or comparative boxplot, a side-by-side boxplot is a visual display comparing the levels (the possible values) of one categorical variable by means of a quantitative variable. | |
C2236 | A Confusion matrix is an N x N matrix used for evaluating the performance of a classification model, where N is the number of target classes. The matrix compares the actual target values with those predicted by the machine learning model. | |
C2237 | To convert a logit ( glm output) to probability, follow these 3 steps:Take glm output coefficient (logit)compute e-function on the logit using exp() “de-logarithimize” (you'll get odds then)convert odds to probability using this formula prob = odds / (1 + odds) . | |
C2238 | The process of dividing each feature by its range is called feature scaling. The process feature scaling is used to standardize each variables individually. The term feature scaling when it comes to data processing is also known as data normalization. | |
C2239 | The probability theory provides a means of getting an idea of the likelihood of occurrence of different events resulting from a random experiment in terms of quantitative measures ranging between zero and one. The probability is zero for an impossible event and one for an event which is certain to occur. | |
C2240 | 80% accurate. Precision - Precision is the ratio of correctly predicted positive observations to the total predicted positive observations. | |
C2241 | As a hypothetical example of systematic sampling, assume that in a population of 10,000 people, a statistician selects every 100th person for sampling. The sampling intervals can also be systematic, such as choosing a new sample to draw from every 12 hours. | |
C2242 | A continuous random variable is a function X X X on the outcomes of some probabilistic experiment which takes values in a continuous set V V V. That is, the possible outcomes lie in a set which is formally (by real-analysis) continuous, which can be understood in the intuitive sense of having no gaps. | |
C2243 | Naive Bayes algorithm works on Bayes theorem and takes a probabilistic approach, unlike other classification algorithms. The algorithm has a set of prior probabilities for each class. Once data is fed, the algorithm updates these probabilities to form something known as posterior probability. | |
C2244 | Convolutional Neural Networks (CNNs) Image segmentation with CNN involves feeding segments of an image as input to a convolutional neural network, which labels the pixels. The CNN cannot process the whole image at once. | |
C2245 | Multitasking is the display of strengths and positive attributes in a multiple number of ways at the same time. Time is money, and so multitasking will help you carry out more tasks and be more competitive. Remember to keep multitasking to a limit you can handle, to ensure focus is maximised in the tasks you carry out. | |
C2246 | The optimal number of clusters can be defined as follow:Compute clustering algorithm (e.g., k-means clustering) for different values of k. For each k, calculate the total within-cluster sum of square (wss).Plot the curve of wss according to the number of clusters k.More items | |
C2247 | The false alarm probability is the probability that exceeds a certain threshold when there is no signal. | |
C2248 | In statistical terminology, this is called skewness. In this case, the average can be significantly influenced by the few values, making it not very representative of the majority of the values in the data set. Under these circumstances, median gives a better representation of central tendency than average. | |
C2249 | The cross product a × b is defined as a vector c that is perpendicular (orthogonal) to both a and b, with a direction given by the right-hand rule and a magnitude equal to the area of the parallelogram that the vectors span. | |
C2250 | As nouns the difference between trial and experiment is that trial is an opportunity to test something out; a test while experiment is a test under controlled conditions made to either demonstrate a known truth, examine the validity of a hypothesis, or determine the efficacy of something previously untried. | |
C2251 | 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. | |
C2252 | It is the sum of the likelihood residuals. At record level, the natural log of the error (residual) is calculated for each record, multiplied by minus one, and those values are totaled. | |
C2253 | The natural logarithm function is negative for values less than one and positive for values greater than one. So yes, it is possible that you end up with a negative value for log-likelihood (for discrete variables it will always be so). | |
C2254 | In statistics, the p-value is the probability of obtaining results at least as extreme as the observed results of a statistical hypothesis test, assuming that the null hypothesis is correct. A smaller p-value means that there is stronger evidence in favor of the alternative hypothesis. | |
C2255 | 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. The standard deviation is calculated as the square root of variance by determining each data point's deviation relative to the mean. | |
C2256 | The likelihood ratio test (LRT) is a statistical test of the goodness-of-fit between two models. A relatively more complex model is compared to a simpler model to see if it fits a particular dataset significantly better. If so, the additional parameters of the more complex model are often used in subsequent analyses. | |
C2257 | The standard normal or z-distribution assumes that you know the population standard deviation. The t-distribution is based on the sample standard deviation. | |
C2258 | AIC and BIC are Information criteria methods used to assess model fit while penalizing the number of estimated parameters. | |
C2259 | The test statistic is used to calculate the p-value. A test statistic measures the degree of agreement between a sample of data and the null hypothesis. Its observed value changes randomly from one random sample to a different sample. This causes the test's p-value to become small enough to reject the null hypothesis. | |
C2260 | In machine learning, boosting is an ensemble meta-algorithm for primarily reducing bias, and also variance in supervised learning, and a family of machine learning algorithms that convert weak learners to strong ones. | |
C2261 | Hidden layers allow for the function of a neural network to be broken down into specific transformations of the data. For example, a hidden layer functions that are used to identify human eyes and ears may be used in conjunction by subsequent layers to identify faces in images. | |
C2262 | The histogram is used for variables whose values are numerical and measured on an interval scale. It is generally used when dealing with large data sets (greater than 100 observations). A histogram can also help detect any unusual observations (outliers) or any gaps in the data. | |
C2263 | The loss function of SVM is very similar to that of Logistic Regression. Looking at it by y = 1 and y = 0 separately in below plot, the black line is the cost function of Logistic Regression, and the red line is for SVM. Please note that the X axis here is the raw model output, θᵀx. | |
C2264 | The total degrees of freedom (dfT) are equal to nT – 1, where nT is the total number of subjects in the design. The between-group degrees of freedom (dfB), which are not absolutely necessary to find, are equal to (j)(k) – 1, where j is the number of levels of variable J and k is the number of levels of variable K. | |
C2265 | Multidimensional scaling (MDS) is a means of visualizing the level of similarity of individual cases of a dataset. MDS is used to translate "information about the pairwise 'distances' among a set of n objects or individuals" into a configuration of n points mapped into an abstract Cartesian space. | |
C2266 | The original AlphaGo demonstrated superhuman Go-playing ability, but needed the expertise of human players to get there. Namely, it used a dataset of more than 100,000 Go games as a starting point for its own knowledge. AlphaGo Zero, by comparison, has only been programmed with the basic rules of Go. | |
C2267 | 7:3021:58Suggested clip · 120 secondsStatQuest: Principal Component Analysis (PCA), Step-by-Step YouTubeStart of suggested clipEnd of suggested clip | |
C2268 | Linear regression is one of the most common techniques of regression analysis. Multiple regression is a broader class of regressions that encompasses linear and nonlinear regressions with multiple explanatory variables. | |
C2269 | Accuracy in Machine Learning Accuracy is the number of correctly predicted data points out of all the data points. Often, accuracy is used along with precision and recall, which are other metrics that use various ratios of true/false positives/negatives. | |
C2270 | Key TakeawaysThe least squares method is a statistical procedure to find the best fit for a set of data points by minimizing the sum of the offsets or residuals of points from the plotted curve.Least squares regression is used to predict the behavior of dependent variables. | |
C2271 | It is a Markov random field. It was translated from statistical physics for use in cognitive science. The Boltzmann machine is based on stochastic spin-glass model with an external field, i.e., a Sherrington–Kirkpatrick model that is a stochastic Ising Model and applied to machine learning. | |
C2272 | The most basic way to use a SVC is with a linear kernel, which means the decision boundary is a straight line (or hyperplane in higher dimensions). | |
C2273 | Machine learning uses neural networks and automated algorithms to predict outcomes. Accuracy of data mining depends on how data is collected. Data Mining produces accurate results which are used by machine learning making machine learning produce better results. | |
C2274 | “Bayesian statistics is a mathematical procedure that applies probabilities to statistical problems. It provides people the tools to update their beliefs in the evidence of new data.” | |
C2275 | The 2nd moment around the mean = Σ(xi – μx)2. The second is the variance. In practice, only the first two moments are ever used in statistics. | |
C2276 | Gaussian Distribution Function The nature of the gaussian gives a probability of 0.683 of being within one standard deviation of the mean. The mean value is a=np where n is the number of events and p the probability of any integer value of x (this expression carries over from the binomial distribution ). | |
C2277 | For example, create a time vector and signal:t = 0:1/100:10-1/100; % Time vector x = sin(2*pi*15*t) + sin(2*pi*40*t); % Signal.y = fft(x); % Compute DFT of x m = abs(y); % Magnitude y(m<1e-6) = 0; p = unwrap(angle(y)); % Phase.More items | |
C2278 | A random variable is a numerical description of the outcome of a statistical experiment. For a discrete random variable, x, the probability distribution is defined by a probability mass function, denoted by f(x). | |
C2279 | The cumulative distribution function (CDF) of random variable X is defined as FX(x)=P(X≤x), for all x∈R.SolutionTo find the CDF, note that. To find P(2<X≤5), we can write P(2<X≤5)=FX(5)−FX(2)=3132−34=732. To find P(X>4), we can write P(X>4)=1−P(X≤4)=1−FX(4)=1−1516=116. | |
C2280 | The term "negative binomial" is likely due to the fact that a certain binomial coefficient that appears in the formula for the probability mass function of the distribution can be written more simply with negative numbers. | |
C2281 | SVMs assume that the data it works with is in a standard range, usually either 0 to 1, or -1 to 1 (roughly). So the normalization of feature vectors prior to feeding them to the SVM is very important. Some libraries recommend doing a 'hard' normalization, mapping the min and max values of a given dimension to 0 and 1. | |
C2282 | A Moving-Average (MA) Process Has a Limited Memory An observation of a moving-average process (the MA in ARIMA) consists of a constant, μ (the long-term mean of the process), plus independent random noise minus a fraction of the previous random noise. | |
C2283 | Supervised learning can be used to teach an algorithm to distinguish spam mail from normal correspondence. Unsupervised: In this type of learning, no training data is provided. The algorithm analyzes a body of data for patterns or common elements. Large amounts of unstructured data can then be sorted and categorized. | |
C2284 | 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. | |
C2285 | Regression is a return to earlier stages of development and abandoned forms of gratification belonging to them, prompted by dangers or conflicts arising at one of the later stages. A young wife, for example, might retreat to the security of her parents' home after her… | |
C2286 | Tensors are simply mathematical objects that can be used to describe physical properties, just like scalars and vectors. In fact tensors are merely a generalisation of scalars and vectors; a scalar is a zero rank tensor, and a vector is a first rank tensor. | |
C2287 | Unsupervised learning is commonly used for finding meaningful patterns and groupings inherent in data, extracting generative features, and exploratory purposes. | |
C2288 | The Poisson distribution can be used to calculate the probabilities of various numbers of "successes" based on the mean number of successes. The mean of the Poisson distribution is μ. The variance is also equal to μ. Thus, for this example, both the mean and the variance are equal to 8. | |
C2289 | To get a p-value we compare our observed test- statistic to the randomization distribution of test- statistics obtained by assuming the null is true. The p-value will be the proportion of test- statistics in the randomization distribution that are as or more extreme than the observed test- statistic. | |
C2290 | Many loss or cost functions are designed with an absolute minimum of 0 possible for "no error" results. So in supervised learning problems of regression and classification, you will rarely see a negative cost function value. But there is no absolute rule against negative costs in principle. | |
C2291 | Yes, residual learning is achieved by simply adding an identity mapping parallel to a layer. | |
C2292 | Negative values for the skewness indicate data that are skewed left and positive values for the skewness indicate data that are skewed right. By skewed left, we mean that the left tail is long relative to the right tail. | |
C2293 | Multi-objective optimization (also known as multi-objective programming, vector optimization, multicriteria optimization, multiattribute optimization or Pareto optimization) is an area of multiple criteria decision making that is concerned with mathematical optimization problems involving more than one objective | |
C2294 | The value of the odds ratio tells you how much more likely someone under 25 might be to make a claim, for example, and the associated confidence interval indicates the degree of uncertainty associated with that ratio. | |
C2295 | Optimizers are algorithms or methods used to change the attributes of your neural network such as weights and learning rate in order to reduce the losses. Optimization algorithms or strategies are responsible for reducing the losses and to provide the most accurate results possible. | |
C2296 | When p is greater than 0.5, the distribution will be positively skewed (the peak will be on the left side of the distribution, with relatively fewer observations on the right). | |
C2297 | 2:107:35Suggested clip · 110 secondsLinear Regression R Program Make Predictions - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C2298 | Microsoft Excel has a few statistical functions that can help you to do linear regression analysis such as LINEST, SLOPE, INTERCPET, and CORREL. Because the LINEST function returns an array of values, you must enter it as an array formula. | |
C2299 | 1| Fast R-CNN Written in Python and C++ (Caffe), Fast Region-Based Convolutional Network method or Fast R-CNN is a training algorithm for object detection. This algorithm mainly fixes the disadvantages of R-CNN and SPPnet, while improving on their speed and accuracy. |
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