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C1800 | To calculate the Sharpe ratio on a portfolio or individual investment, you first calculate the expected return for the investment. You then subtract the risk free rate from the expected return, then divide this sum by the standard deviation of the of the portfolio or individual investment. This gives you the ratio. | |
C1801 | 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. By calculating expected values, investors can choose the scenario most likely to give the desired outcome. | |
C1802 | K-means algorithm can be summarized as follow:Specify the number of clusters (K) to be created (by the analyst)Select randomly k objects from the dataset as the initial cluster centers or means.Assigns each observation to their closest centroid, based on the Euclidean distance between the object and the centroid.More items | |
C1803 | Skewness refers to distortion or asymmetry in a symmetrical bell curve, or normal distribution, in a set of data. If the curve is shifted to the left or to the right, it is said to be skewed. Skewness can be quantified as a representation of the extent to which a given distribution varies from a normal distribution. | |
C1804 | remove outliers data.Do feature selection, some of features may not be as informative.May be the linear regression under fitting or over fitting the data you can check ROC curve and try to use more complex model like polynomial regression or regularization respectively. | |
C1805 | The top 5 AI developments as chosen by our team are as follows:The increased speed of AI-enabled medical research. Computer vision, image, and video analysis technology is evolving. Powerful AI-based tools become mainstream. AI learns increasingly higher-level human functions.More items• | |
C1806 | In fact, we show that they coincide precisely when the confusion matrix is perfectly symmetric. In other situations, however, their behaviour can diverge to the point that Kappa should be avoided as a measure of behaviour to compare classifiers in favor of more robust measures as MCC. | |
C1807 | Low-rank approximation is thus a way to recover the "original" (the "ideal" matrix before it was messed up by noise etc.) low-rank matrix i.e., find the matrix that is most consistent (in terms of observed entries) with the current matrix and is low-rank so that it can be used as an approximation to the ideal matrix. | |
C1808 | A histogram looks like a bar chart , except the area of the bar, and not the height, shows the frequency of the data . Histograms are typically used when the data is in groups of unequal width. This is called frequency density. | |
C1809 | Even when multicollinearity is great, the least-squares regression equation can be highly predictive. So, if you are only interested in prediction, multicollinearity is not a problem. | |
C1810 | The purpose of factor analysis is to reduce many individual items into a fewer number of dimensions. Factor analysis can be used to simplify data, such as reducing the number of variables in regression models. Most often, factors are rotated after extraction. | |
C1811 | Features: The characteristics that define your problem. These are also called attributes. Parameters: The variables your algorithm is trying to tune to build an accurate model. | |
C1812 | The “Linear-by-Linear Association” statistic is used when the variables are ordinal, but many simply use the Pearson for those as well. Column 2 shows the Chi Square values for each alternative test. The main one of interest is the Pearson Chi-Square value of . | |
C1813 | Machine learning algorithms are almost always optimized for raw, detailed source data. Thus, the data environment must provision large quantities of raw data for discovery-oriented analytics practices such as data exploration, data mining, statistics, and machine learning. | |
C1814 | There are two types of methods used for image processing namely, analogue and digital image processing. Image analysts use various fundamentals of interpretation while using these visual techniques. | |
C1815 | If your regression model contains independent variables that are statistically significant, a reasonably high R-squared value makes sense. The statistical significance indicates that changes in the independent variables correlate with shifts in the dependent variable. | |
C1816 | Not usually. SGD tends to perform better than using line search. | |
C1817 | 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 negatively skewed distribution, the mean is usually less than the median because the few low scores tend to shift the mean to the left. | |
C1818 | Random effect models assist in controlling for unobserved heterogeneity when the heterogeneity is constant over time and not correlated with independent variables. Two common assumptions can be made about the individual specific effect: the random effects assumption and the fixed effects assumption. | |
C1819 | Abstract. This work centers on a novel data mining technique we term supervised clustering. Unlike traditional clustering, supervised clustering assumes that the examples are classified. The goal of supervised clustering is to identify class-uniform clusters that have high probability densities. | |
C1820 | The rectified linear activation function or ReLU for short is a piecewise linear function that will output the input directly if it is positive, otherwise, it will output zero. The rectified linear activation is the default activation when developing multilayer Perceptron and convolutional neural networks. | |
C1821 | Time series analysis, on the other hand, is a field of statistics/econometrics where we try to understand trends in time series data , draw graphs : and make predictions out of it (via linear regression, ARIMA, etc..). In that way, we could say that it's more of a supervised approach. | |
C1822 | Word embeddings are created using a neural network with one input layer, one hidden layer and one output layer. The computer does not understand that the words king, prince and man are closer together in a semantic sense than the words queen, princess, and daughter. All it sees are encoded characters to binary. | |
C1823 | Normal Distribution is a probability distribution where probability of x is highest at centre and lowest in the ends whereas in Uniform Distribution probability of x is constant. Uniform Distribution is a probability distribution where probability of x is constant. | |
C1824 | Distributed representation describes the same data features across multiple scalable and interdependent layers. Each layer defines the information with the same level of accuracy, but adjusted for the level of scale. These layers are learned concurrently but in a non-linear fashion. | |
C1825 | Fixed effects models remove omitted variable bias by measuring changes within groups across time, usually by including dummy variables for the missing or unknown characteristics. | |
C1826 | According to gradient descent rule, we should update the weight according to w = w - df/dw. | |
C1827 | If an overestimate or underestimate does happen, the mean of the difference is called a “bias.” That's just saying if the estimator (i.e. the sample mean) equals the parameter (i.e. the population mean), then it's an unbiased estimator. | |
C1828 | K-fold cross-validationRandomly split the data set into k-subsets (or k-fold) (for example 5 subsets)Reserve one subset and train the model on all other subsets.Test the model on the reserved subset and record the prediction error.Repeat this process until each of the k subsets has served as the test set.More items• | |
C1829 | Parametric tests are those that make assumptions about the parameters of the population distribution from which the sample is drawn. This is often the assumption that the population data are normally distributed. Non-parametric tests are “distribution-free” and, as such, can be used for non-Normal variables. | |
C1830 | An example of numerical data would be the number of people that attended the movie theater over the course of a month. You can also put data in ascending (least to greatest) and descending (greatest to least) order. Data can only be numerical if the answers can be represented in fraction and/or decimal form. | |
C1831 | Max pooling is a sample-based discretization process. The objective is to down-sample an input representation (image, hidden-layer output matrix, etc.), reducing its dimensionality and allowing for assumptions to be made about features contained in the sub-regions binned. | |
C1832 | Digital Signal Processing is important because it significantly increases the overall value of hearing protection. Unlike passive protection, DSP suppresses noise without blocking the speech signal. | |
C1833 | Statistical inference can be divided into two areas: estimation and hypothesis testing. In estimation, the goal is to describe an unknown aspect of a population, for example, the average scholastic aptitude test (SAT) writing score of all examinees in the State of California in the USA. | |
C1834 | As the name suggests, GLM models are the generalization of the linear regression model. we mean that rather than forcing a linear relationship between the dependent and independent variables, it allows the dependent variable to be related with the independent variables through a link function. | |
C1835 | Logarithmic Loss, or simply Log Loss, is a classification loss function often used as an evaluation metric in Kaggle competitions. Log Loss quantifies the accuracy of a classifier by penalising false classifications. | |
C1836 | Kinesthetic learners are the most hands-on learning type. They learn best by doing and may get fidgety if forced to sit for long periods of time. Kinesthetic learners do best when they can participate in activities or solve problems in a hands-on manner. | |
C1837 | Any dataset with an unequal class distribution is technically imbalanced. However, a dataset is said to be imbalanced when there is a significant, or in some cases extreme, disproportion among the number of examples of each class of the problem. | |
C1838 | Descriptive statistics help us to simplify large amounts of data in a sensible way. Each descriptive statistic reduces lots of data into a simpler summary. For instance, consider a simple number used to summarize how well a batter is performing in baseball, the batting average. | |
C1839 | The false discovery rate is the ratio of the number of false positive results to the number of total positive test results. Out of 10,000 people given the test, there are 450 true positive results (box at top right) and 190 false positive results (box at bottom right) for a total of 640 positive results. | |
C1840 | The median filter is a non-linear digital filtering technique, often used to remove noise from an image or signal. Such noise reduction is a typical pre-processing step to improve the results of later processing (for example, edge detection on an image). | |
C1841 | Cross-sectional data are the result of a data collection, carried out at a single point in time on a statistical unit. With cross-sectional data, we are not interested in the change of data over time, but in the current, valid opinion of the respondents about a question in a survey. | |
C1842 | A paired t-test is used when we are interested in the difference between two variables for the same subject. Often the two variables are separated by time. For example, in the Dixon and Massey data set we have cholesterol levels in 1952 and cholesterol levels in 1962 for each subject. | |
C1843 | POS tagging is the process of marking up a word in a corpus to a corresponding part of a speech tag, based on its context and definition. This task is not straightforward, as a particular word may have a different part of speech based on the context in which the word is used. | |
C1844 | There are multiple ways to select a good starting point for the learning rate. A naive approach is to try a few different values and see which one gives you the best loss without sacrificing speed of training. We might start with a large value like 0.1, then try exponentially lower values: 0.01, 0.001, etc. | |
C1845 | fastText is another word embedding method that is an extension of the word2vec model. Instead of learning vectors for words directly, fastText represents each word as an n-gram of characters. This helps capture the meaning of shorter words and allows the embeddings to understand suffixes and prefixes. | |
C1846 | The number of units is a parameter in the LSTM, referring to the dimensionality of the hidden state and dimensionality of the output state (they must be equal). a LSTM comprises an entire layer. | |
C1847 | AUC stands for "Area under the ROC Curve." That is, AUC measures the entire two-dimensional area underneath the entire ROC curve (think integral calculus) from (0,0) to (1,1). Figure 5. AUC (Area under the ROC Curve). AUC provides an aggregate measure of performance across all possible classification thresholds. | |
C1848 | People always think crime is increasing” even if it's not. He addresses the logical fallacy of confirmation bias, explaining that people's tendency, when testing a hypothesis they're inclined to believe, is to seek examples confirming it. “Most people think they're not like other people. | |
C1849 | Ensemble learning methods are widely used nowadays for its predictive performance improvement. Ensemble learning combines multiple predictions (forecasts) from one or multiple methods to overcome accuracy of simple prediction and to avoid possible overfit. | |
C1850 | We explore six challenges for neural machine translation: domain mismatch, amount of training data, rare words, long sentences, word alignment, and beam search. | |
C1851 | When we know an input value and want to determine the corresponding output value for a function, we evaluate the function. When we know an output value and want to determine the input values that would produce that output value, we set the output equal to the function's formula and solve for the input. | |
C1852 | The inductive bias (also known as learning bias) of a learning algorithm is the set of assumptions that the learner uses to predict outputs of given inputs that it has not encountered. In machine learning, one aims to construct algorithms that are able to learn to predict a certain target output. | |
C1853 | We use factorials when we look at permutations and combinations. Permutations tell us how many different ways we can arrange things if their order matters. Combinations tells us how many ways we can choose k item from n items if their order does not matter. | |
C1854 | You simply measure the number of correct decisions your classifier makes, divide by the total number of test examples, and the result is the accuracy of your classifier. It's that simple. The vast majority of research results report accuracy, and many practical projects do too. | |
C1855 | The output layer is responsible for producing the final result. There must always be one output layer in a neural network. The output layer takes in the inputs which are passed in from the layers before it, performs the calculations via its neurons and then the output is computed. | |
C1856 | From the menus of SPSS choose: Analyze Scale Multidimensional Scaling… In Distances, select either Data are distances or Create distances from data. If your data are distances, you must select at least four numeric variables for analysis, and you can click Shape to indicate the shape of the distance matrix. | |
C1857 | Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data. Specifically, underfitting occurs if the model or algorithm shows low variance but high bias. Underfitting is often a result of an excessively simple model. | |
C1858 | Supervised learning allows collecting data and produce data output from the previous experiences. Helps to optimize performance criteria with the help of experience. Supervised machine learning helps to solve various types of real-world computation problems. | |
C1859 | Definition. A convenience sample is a type of non-probability sampling method where the sample is taken from a group of people easy to contact or to reach. For example, standing at a mall or a grocery store and asking people to answer questions would be an example of a convenience sample. | |
C1860 | The weighted kappa is calculated using a predefined table of weights which measure the degree of disagreement between the two raters, the higher the disagreement the higher the weight. | |
C1861 | Go to 'Filter > Blur > Gaussian Blur…' and the 'Gaussian Blur' window will appear. You can drag the image in the 'Gaussian Blur' window to look for the object you're going to blur. If you find it too small, tick the 'Preview' box and the result of the 'Gaussian Filter' blur will be visible in the image. | |
C1862 | Nonetheless, they are not the same. Standard deviation is used to measure the spread of data around the mean, while RMSE is used to measure distance between some values and prediction for those values. If you use mean as your prediction for all the cases, then RMSE and SD will be exactly the same. | |
C1863 | Discrete variables are countable in a finite amount of time. For example, you can count the change in your pocket. You can count the money in your bank account. You could also count the amount of money in everyone's bank accounts. | |
C1864 | The “regular” normal distribution has one random variable; A bivariate normal distribution is made up of two independent random variables. The two variables in a bivariate normal are both are normally distributed, and they have a normal distribution when both are added together. | |
C1865 | For classification: As a general rule, the more the hidden layers, the better the network. But, as the hidden layers increase, your network becomes data hungry. So, your dataset should have sufficient number of samples to feed the hungry network. Otherwise your network will overfit the training set. | |
C1866 | Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for explorative rapid qualitative data analysis and interactive data visualization. | |
C1867 | In natural language processing, the latent Dirichlet allocation (LDA) is a generative statistical model that allows sets of observations to be explained by unobserved groups that explain why some parts of the data are similar. | |
C1868 | Here are some important considerations while choosing an algorithm.Size of the training data. It is usually recommended to gather a good amount of data to get reliable predictions. Accuracy and/or Interpretability of the output. Speed or Training time. Linearity. Number of features. | |
C1869 | Variance (σ2) in statistics is a measurement of the spread between numbers in a data set. That is, it measures how far each number in the set is from the mean and therefore from every other number in the set. | |
C1870 | The ACF stands for Autocorrelation function, and the PACF for Partial Autocorrelation function. Looking at these two plots together can help us form an idea of what models to fit. Autocorrelation computes and plots the autocorrelations of a time series. | |
C1871 | Lets find out the model of the binomial distribution who PMF is:f(x) = C(n, x)p^x(1-p)^(n-x) where 0<= p <= 1 and x = 0(1)n.Note that when p=0, f(0)=1 and f(x)=0 when x> 0. So, binomial distribution is unimodal if p = 0 or p=1.But what happens when 0<p<1?In such cases,f(x+1)/f(x) = (n-x)p / [(x+1)(1-p)]More items | |
C1872 | fits that relationship. That line is called a Regression Line and has the equation ŷ= a + b x. The Least Squares Regression Line is the line that makes the vertical distance from the data points to the regression line as small as possible. | |
C1873 | The binomial distribution is a probability distribution that summarizes the likelihood that a value will take one of two independent values under a given set of parameters or assumptions. | |
C1874 | Principle Component Analysis (PCA) is a common feature extraction method in data science. Technically, PCA finds the eigenvectors of a covariance matrix with the highest eigenvalues and then uses those to project the data into a new subspace of equal or less dimensions. | |
C1875 | Qualitative Variables. Also known as categorical variables, qualitative variables are variables with no natural sense of ordering. They are therefore measured on a nominal scale. For instance, hair color (Black, Brown, Gray, Red, Yellow) is a qualitative variable, as is name (Adam, Becky, Christina, Dave . . .). | |
C1876 | The main differences therefore are that Gradient Boosting is a generic algorithm to find approximate solutions to the additive modeling problem, while AdaBoost can be seen as a special case with a particular loss function. Hence, gradient boosting is much more flexible. | |
C1877 | A weighted average (weighted mean or scaled average) is used when we consider some data values to be more important than other values and so we want them to contribute more to the final "average". This often occurs in the way some professors or teachers choose to assign grades in their courses. | |
C1878 | The converse of Theorem 1 is the following: Given vector field F = Pi + Qj on D with C1 coefficients, if Py = Qx, then F is the gradient of some function. | |
C1879 | In statistics, the number of degrees of freedom is the number of values in the final calculation of a statistic that are free to vary. The number of independent ways by which a dynamic system can move, without violating any constraint imposed on it, is called number of degrees of freedom. | |
C1880 | The formula for conditional probability is derived from the probability multiplication rule, P(A and B) = P(A)*P(B|A). You may also see this rule as P(A∪B). The Union symbol (∪) means “and”, as in event A happening and event B happening. | |
C1881 | According to Cohen's original article, values ≤ 0 as indicating no agreement and 0.01–0.20 as none to slight, 0.21–0.40 as fair, 0.41– 0.60 as moderate, 0.61–0.80 as substantial, and 0.81–1.00 as almost perfect agreement. | |
C1882 | Correlation coefficient values below 0.3 are considered to be weak; 0.3-0.7 are moderate; >0.7 are strong. You also have to compute the statistical significance of the correlation. | |
C1883 | Mean Symbol With Alt Codes Type the letter "x," hold the Alt key and type "0772" into the number pad. This adds the bar symbol to the x. | |
C1884 | A continuous distribution has a range of values that are infinite, and therefore uncountable. For example, time is infinite: you could count from 0 seconds to a billion seconds…a trillion seconds…and so on, forever. | |
C1885 | Gradient Descent is the most basic but most used optimization algorithm. It's used heavily in linear regression and classification algorithms. Backpropagation in neural networks also uses a gradient descent algorithm. | |
C1886 | (1) False-positive results may occur in patients with prior infection with M marinum, M szulgai, or M kansasii. Negative: No IFN-gamma response to M tuberculosis antigens was detected. Infection with M tuberculosis is unlikely. | |
C1887 | Introduction. This method determines the chloride ion concentration of a solution by titration with silver nitrate. As the silver nitrate solution is slowly added, a precipitate of silver chloride forms. Ag+(aq) + Cl–(aq) → AgCl(s) The end point of the titration occurs when all the chloride ions are precipitated. | |
C1888 | The sensitivity and specificity of a test often vary with disease prevalence; this effect is likely to be the result of mechanisms, such as patient spectrum, that affect prevalence, sensitivity and specificity. | |
C1889 | Minimum description length (MDL) refers to various formalizations of Occam's razor based on formal languages used to parsimoniously describe data. In its most basic form, MDL is a model selection principle: the shortest description of the data as the best model. | |
C1890 | Despite having similar aims and processes, there are two main differences between them: Machine learning works out predictions and recalibrates models in real-time automatically after design. Meanwhile, predictive analytics works strictly on “cause” data and must be refreshed with “change” data. | |
C1891 | Autoregression is a time series model that uses observations from previous time steps as input to a regression equation to predict the value at the next time step. It is a very simple idea that can result in accurate forecasts on a range of time series problems. | |
C1892 | 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. When a biased estimator is used, bounds of the bias are calculated. | |
C1893 | (regression, Anova, location problems?) Typically when an assumption is made that the error is normally distributed the reason lies in history: assuming the error structure was normal made the work required to develop test statistics, estimates, and other calculations, relatively easy. | |
C1894 | Given sufficient training data (often hundreds or thousands of images per label), an image classification model can learn to predict whether new images belong to any of the classes it has been trained on. This process of prediction is called inference. | |
C1895 | Variables are the factors in a experiment that change or potentially change. There are two types of variables independent and dependent, these variables can also be viewed as the cause and effect of an experiment. | |
C1896 | Ordinary least-square regression has no normality requirement. | |
C1897 | Negative binomial regression – Negative binomial regression can be used for over-dispersed count data, that is when the conditional variance exceeds the conditional mean. | |
C1898 | The difference between quota sampling and stratified sampling is: although both "group" participants by an important characteristic, stratified sampling relies on random selection within each group, while quota sampling relies on convenience sampling within each group. | |
C1899 | Density values can be greater than 1. In the frequency histogram the y-axis was percentage, but in the density curve the y-axis is density and the area gives the percentage. When creating the density curve the values on the y-axis are calculated (scaled) so that the total area under the curve is 1. |
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