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C900 | The outcome variable is also called the response or dependent variable, and the risk factors and confounders are called the predictors, or explanatory or independent variables. In regression analysis, the dependent variable is denoted "Y" and the independent variables are denoted by "X". | |
C901 | distribution free test | |
C902 | The metric system uses units such as meter, liter, and gram to measure length, liquid volume, and mass, just as the U.S. customary system uses feet, quarts, and ounces to measure these. | |
C903 | The nominator is the joint probability and the denominator is the probability of the given outcome. This is the conditional probability: P(A∣B)=P(A∩B)P(B) This is the Bayes' rule: P(A∣B)=P(B|A)∗P(A)P(B). | |
C904 | We analyze the expected run time because it represents the more typical time cost. | |
C905 | Stanley F. Schmidt is generally credited with developing the first implementation of a Kalman filter. He realized that the filter could be divided into two distinct parts, with one part for time periods between sensor outputs and another part for incorporating measurements. | |
C906 | Real-time processing is the process in which a system can input rapidly changing data and then provide output instantaneously so that the change over time can be seen very quickly. Real-time data processing is a method that is used when data input requests need to be dealt with quickly. | |
C907 | The first thing you need to do is learn a programming language. Though there are a lot of languages that you can start with, Python is what many prefer to start with because its libraries are better suited to Machine Learning. Here are some good resources for Python: CodeAcademy. | |
C908 | How to Annotate an Image in WordIn the “Illustrations” section, click “Pictures”. The cursor changes to a big “+” symbol. Right-click on the callout and select “Fill” from the popup box above the popup menu. Once you've moved the callout, you may need to reposition the callout arrow to point where you want. | |
C909 | Now the centripetal acceleration is given by the second expression in. ac=v2r;ac=rω2 a c = v 2 r ; a c = r ω 2 as ac = rω2. | |
C910 | Parametric tests involve specific probability distributions (e.g., the normal distribution) and the tests involve estimation of the key parameters of that distribution (e.g., the mean or difference in means) from the sample data. | |
C911 | Covariate shift refers to the change in the distribution of the input variables present in the training and the test data. It is the most common type of shift and it is now gaining more attention as nearly every real-world dataset suffers from this problem. | |
C912 | Advertisements. Multi-Layer perceptron defines the most complicated architecture of artificial neural networks. It is substantially formed from multiple layers of perceptron. | |
C913 | Explanation: Entropy (S) by the modern definition is the amount of energy dispersal in a system. Therefore, the system entropy will increase when the amount of motion within the system increases. For example, the entropy increases when ice (solid) melts to give water (liquid). | |
C914 | Because a researcher rarely has direct access to the entire population of interest in social science research, a researcher must rely upon a sampling frame to represent all of the elements of the population of interest. Generally, sampling frames can be divided into two types, list and nonlist. | |
C915 | A model represents what was learned by a machine learning algorithm. The model is the “thing” that is saved after running a machine learning algorithm on training data and represents the rules, numbers, and any other algorithm-specific data structures required to make predictions. | |
C916 | In the visual system, visual receptive fields are volumes in visual space. The receptive field is often identified as the region of the retina where the action of light alters the firing of the neuron. | |
C917 | The linear, polynomial and RBF or Gaussian kernel are simply different in case of making the hyperplane decision boundary between the classes. Usually linear and polynomial kernels are less time consuming and provides less accuracy than the rbf or Gaussian kernels. | |
C918 | A stratified random sampling involves dividing the entire population into homogeneous groups called strata (plural for stratum). Random samples are then selected from each stratum. A random sample from each stratum is taken in a number proportional to the stratum's size when compared to the population. | |
C919 | Two sets A and B are called disjoint if A and B have no elements in common. Another equivalent definition of disjoint sets would be that the intersection of the two sets actually equals the empty set. | |
C920 | Poisson regression – Poisson regression is often used for modeling count data. Poisson regression has a number of extensions useful for count models. Negative binomial regression – Negative binomial regression can be used for over-dispersed count data, that is when the conditional variance exceeds the conditional mean. | |
C921 | Hadoop Examples: 5 Real-World Use CasesFinancial services companies use analytics to assess risk, build investment models, and create trading algorithms; Hadoop has been used to help build and run those applications.Retailers use it to help analyze structured and unstructured data to better understand and serve their customers.More items• | |
C922 | For an upper-tailed test, the p-value is equal to one minus this probability; p-value = 1 - cdf(ts). For a two-sided test, the p-value is equal to two times the p-value for the lower-tailed p-value if the value of the test statistic from your sample is negative. | |
C923 | The model works by first splitting the input image into a grid of cells, where each cell is responsible for predicting a bounding box if the center of a bounding box falls within it. Each grid cell predicts a bounding box involving the x, y coordinate and the width and height and the confidence. | |
C924 | Ratio scales are like interval scales except they have true zero points. A good example is the Kelvin scale of temperature. This scale has an absolute zero. | |
C925 | Descriptive analytics is a statistical method that is used to search and summarize historical data in order to identify patterns or meaning. | |
C926 | For this, you aim to maximize the Youden's index, which is Maximum=Sensitivity + Specificity - 1. So you choose those value of the ROC-curve as a cut-off, where the term "Sensitivity + Specificity - 1" (parameters taken from the output in the same line as the observed value, see attachments) is maximal. | |
C927 | Your classifier would have learned an equal an opposite rule, with the same performance and same AUC / ROC curve. | |
C928 | When you reject the null hypothesis with a t-test, you are saying that the means are statistically different. The difference is meaningful. Chi Square: When you reject the null hypothesis with a Chi-Square, you are saying that there is a relationship between the two variables. | |
C929 | Interpreting the Range The range is interpreted as the overall dispersion of values in a dataset or, more literally, as the difference between the largest and the smallest value in a dataset. The range is measured in the same units as the variable of reference and, thus, has a direct interpretation as such. | |
C930 | LSTMs solve the problem using a unique additive gradient structure that includes direct access to the forget gate's activations, enabling the network to encourage desired behaviour from the error gradient using frequent gates update on every time step of the learning process. | |
C931 | A Logit function, also known as the log-odds function, is a function that represents probability values from 0 to 1, and negative infinity to infinity. The function is an inverse to the sigmoid function that limits values between 0 and 1 across the Y-axis, rather than the X-axis. | |
C932 | ANCOVA and multiple linear regression are similar, but regression is more appropriate when the emphasis is on the dependent outcome variable, while ANCOVA is more appropriate when the emphasis is on comparing the groups from one of the independent variables. | |
C933 | Jakob Bernoulli | |
C934 | Correspondence analysis reveals the relative relationships between and within two groups of variables, based on data given in a contingency table. For brand perceptions, these two groups are brands and the attributes that apply to these brands. | |
C935 | 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. | |
C936 | Histograms are generally used to show the results of a continuous data set such as height, weight, time, etc. A bar graph has spaces between the bars, while a histogram does not. A histogram often shows the frequency that an event occurs within the defined range. It shows you how many times that event happens. | |
C937 | Structured data is clearly defined and searchable types of data, while unstructured data is usually stored in its native format. Structured data is quantitative, while unstructured data is qualitative. Structured data is often stored in data warehouses, while unstructured data is stored in data lakes. | |
C938 | Machine learning is perhaps the principal technology behind two emerging domains: data science and artificial intelligence. The rise of machine learning is coming about through the availability of data and computation, but machine learning methdologies are fundamentally dependent on models. | |
C939 | Gradient Descent with Momentum considers the past gradients to smooth out the update. It computes an exponentially weighted average of your gradients, and then use that gradient to update your weights instead. | |
C940 | Neural nets are a means of doing machine learning, in which a computer learns to perform some task by analyzing training examples. Most of today's neural nets are organized into layers of nodes, and they're “feed-forward,” meaning that data moves through them in only one direction. | |
C941 | Discretion traces back to the Latin verb discernere, "to separate, to discern," from the prefix dis-, "off, away," plus cernere, "separate, sift." If you use discretion, you sift away what is not desirable, keeping only the good. | |
C942 | A probability sampling method is any method of sampling that utilizes some form of random selection. In order to have a random selection method, you must set up some process or procedure that assures that the different units in your population have equal probabilities of being chosen. | |
C943 | Marginal effect is a measure of the instantaneous effect that a change in a particular explanatory variable has on the predicted probability of , when the other covariates are kept fixed. | |
C944 | word2vec itself is a simple bi-layered neural network architecture, it turns text into meaningful vectors form that deeper networks can understand. In other words the out put of simple neural word2vec model is used as input for Deep Networks. | |
C945 | A decision making threshold is the value of the decision making variable at which the decision is made, such that an action is selected or a commitment to one alternative is made, marking the end of accumulation of information. | |
C946 | Partitioning methods Horizontal partitioning involves putting different rows into different tables. Vertical partitioning involves creating tables with fewer columns and using additional tables to store the remaining columns. | |
C947 | Top Applications of Deep Learning Across IndustriesSelf Driving Cars.News Aggregation and Fraud News Detection.Natural Language Processing.Virtual Assistants.Entertainment.Visual Recognition.Fraud Detection.Healthcare.More items• | |
C948 | Ridge Regression is a technique for analyzing multiple regression data that suffer from multicollinearity. When multicollinearity occurs, least squares estimates are unbiased, but their variances are large so they may be far from the true value. | |
C949 | Reinforcement learning is the training of machine learning models to make a sequence of decisions. The agent learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an artificial intelligence faces a game-like situation. | |
C950 | The ROC curve shows the trade-off between sensitivity (or TPR) and specificity (1 – FPR). Classifiers that give curves closer to the top-left corner indicate a better performance. The closer the curve comes to the 45-degree diagonal of the ROC space, the less accurate the test. | |
C951 | Neo is contacted by Trinity (Carrie-Anne Moss), a beautiful stranger who leads him into an underworld where he meets Morpheus. They fight a brutal battle for their lives against a cadre of viciously intelligent secret agents. It is a truth that could cost Neo something more precious than his life. | |
C952 | Unsupervised learning is very useful in exploratory analysis because it can automatically identify structure in data. Dimensionality reduction, which refers to the methods used to represent data using less columns or features, can be accomplished through unsupervised methods. | |
C953 | The standard error of the sample mean depends on both the standard deviation and the sample size, by the simple relation SE = SD/√(sample size). | |
C954 | K-Nearest Neighbors Underfitting and Overfitting The value of k in the KNN algorithm is related to the error rate of the model. Overfitting imply that the model is well on the training data but has poor performance when new data is coming. | |
C955 | A stratified random sampling involves dividing the entire population into homogeneous groups called strata (plural for stratum). A random sample from each stratum is taken in a number proportional to the stratum's size when compared to the population. These subsets of the strata are then pooled to form a random sample. | |
C956 | 15 Most Used Machine Learning Tools By ExpertsKnime. Knime is again an open-source machine learning tool that is based on GUI. Accord.net. Accord.net is a computational machine learning framework. Scikit-Learn. Scikit-Learn is an open-source machine learning package. TensorFlow. Weka. Pytorch. RapidMiner. Google Cloud AutoML.More items• | |
C957 | Spearman Rank Correlation: Worked Example (No Tied Ranks)The formula for the Spearman rank correlation coefficient when there are no tied ranks is: Step 1: Find the ranks for each individual subject. Step 2: Add a third column, d, to your data. Step 5: Insert the values into the formula.More items• | |
C958 | Adversarial examples are inputs to machine learning models that an attacker has intentionally designed to cause the model to make a mistake; they're like optical illusions for machines. | |
C959 | You can see SVM as an instance-based learning algorithm because you need to memorize the support vectors if you cannot represent the feature space and hence the discriminating hyperplane in this space explicitly. | |
C960 | A random variable, usually written X, is a variable whose possible values are numerical outcomes of a random phenomenon. There are two types of random variables, discrete and continuous. | |
C961 | Well labeled dataset can be used to train a custom model.In the Data Labeling Service UI, you create a dataset and import items into it from the same page.Open the Data Labeling Service UI. Click the Create button in the title bar.On the Add a dataset page, enter a name and description for the dataset.More items | |
C962 | Extrapolation is an estimation of a value based on extending a known sequence of values or facts beyond the area that is certainly known. Interpolation is an estimation of a value within two known values in a sequence of values. Polynomial interpolation is a method of estimating values between known data points. | |
C963 | To “converge” in machine learning is to have an error so close to local/global minimum, or you can see it aa having a performance so clise to local/global minimum. When the model “converges” there is usually no significant error decrease / performance increase anymore. ( Unless a more modern optimizer is applied) | |
C964 | An Iterator is an object that can be used to loop through collections, like ArrayList and HashSet. It is called an "iterator" because "iterating" is the technical term for looping. To use an Iterator, you must import it from the java. | |
C965 | In statistics, an outlier is a data point that differs significantly from other observations. An outlier may be due to variability in the measurement or it may indicate experimental error; the latter are sometimes excluded from the data set. An outlier can cause serious problems in statistical analyses. | |
C966 | Random utility theory is based on the hypothesis that every individual is a rational decision-maker, maximizing utility relative to his or her choices. Specifically, the theory is based on the following assumptions. | |
C967 | Definition: Hadoop is a kind of framework that can handle the huge volume of Big Data and process it, whereas Big Data is just a large volume of the Data which can be in unstructured and structured data. | |
C968 | One-vs-rest (OvR for short, also referred to as One-vs-All or OvA) is a heuristic method for using binary classification algorithms for multi-class classification. It involves splitting the multi-class dataset into multiple binary classification problems. | |
C969 | Statistical researchers often use a linear relationship to predict the (average) numerical value of Y for a given value of X using a straight line (called the regression line). If you know the slope and the y-intercept of that regression line, then you can plug in a value for X and predict the average value for Y. | |
C970 | In the nervous system, dendrites, branches of neurons that transmit signals between synapses and soma, play a critical role in processing functions, such as nonlinear integration of postsynaptic signals. | |
C971 | Overfitting refers to a model that models the training data too well. Overfitting happens when a model learns the detail and noise in the training data to the extent that it negatively impacts the performance of the model on new data. | |
C972 | The receptive field in Convolutional Neural Networks (CNN) is the region of the input space that affects a particular unit of the network. The numbers inside the pixels on the left image represent how many times this pixel was part of a convolution step (each sliding step of the filter). | |
C973 | An autocorrelation plot is designed to show whether the elements of a time series are positively correlated, negatively correlated, or independent of each other. (The prefix auto means “self”— autocorrelation specifically refers to correlation among the elements of a time series.) | |
C974 | The sample variance is an estimator for the population variance. When applied to sample data, the population variance formula is a biased estimator of the population variance: it tends to underestimate the amount of variability. We are using one fitted value (sample mean) in our estimate of the variance. | |
C975 | The Structural Topic Model allows researchers to flexibly estimate a topic model that includes document-level metadata. The stm package provides many useful features, including rich ways to explore topics, estimate uncertainty, and visualize quantities of interest. | |
C976 | Z = Given Z value. p = Percentage of population. C = Confidence level. Pop = Population.Sample Size Formula for Infinite and Finite Population.Formulas for Sample Size (SS)For Infinite Sample SizeSS = [Z2p (1 − p)]/ C2For Finite Sample SizeSS/ [1 + {(SS − 1)/Pop}] | |
C977 | Consider a binomial distribution with parameters (n, p). When n is large and p is small , approximate the probability using Poisson distribution. When n is large and p is close to 0.5, use normal approximation. | |
C978 | Each sample contains different elements so the value of the sample statistic differs for each sample selected. These statistics provide different estimates of the parameter. The sampling distribution describes how these different values are distributed. | |
C979 | Value (V): Vπ(s) is defined as the expected value of the cumulative reward (discounted) that an agent will receive if he starts in state s at t = 0 and follows policy π. Vπ(s) is also called state value function or value function. The value function estimates value of a state. | |
C980 | If the points on the scatter plot seem to form a line that slants down from left to right, there is a negative relationship or negative correlation between the variables. If the points on the scatter plot seem to be scattered randomly, there is no relationship or no correlation between the variables. | |
C981 | In this context, a neural network is one of several machine learning algorithms that can help solve classification problems. Its unique strength is its ability to dynamically create complex prediction functions, and emulate human thinking, in a way that no other algorithm can. | |
C982 | Decision Trees bisect the space into smaller and smaller regions, whereas Logistic Regression fits a single line to divide the space exactly into two. A single linear boundary can sometimes be limiting for Logistic Regression. | |
C983 | Google built the underlying TensorFlow software with the C++ programming language. But in developing applications for this AI engine, coders can use either C++ or Python, the most popular language among deep learning researchers. | |
C984 | 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). | |
C985 | Asymptotic analysis of an algorithm refers to defining the mathematical boundation/framing of its run-time performance. Asymptotic analysis is input bound i.e., if there's no input to the algorithm, it is concluded to work in a constant time. Other than the "input" all other factors are considered constant. | |
C986 | Statistics, when used in a misleading fashion, can trick the casual observer into believing something other than what the data shows. That is, a misuse of statistics occurs when a statistical argument asserts a falsehood. In some cases, the misuse may be accidental. | |
C987 | Data structure and algorithms help in understanding the nature of the problem at a deeper level and thereby a better understanding of the world. If you want to know more about Why Data Structures and Algorithms then you must watch this video of Mr. | |
C988 | In hierarchical k-means we pick some k to be the branching factor. at each level of the clustering hierarchy. We then clus- ter the set of points into k clusters using a standard k- means algorithm. Finally, we recursively cluster each sub-cluster until we hit some small fixed number of points. | |
C989 | Data Science Interview Questions based on AUC. AUC stands for Area Under the Curve. The way it is done is to see how much area has been covered by the ROC curve. If we obtain a perfect classifier, then the AUC score is 1.0. If the classifier is random in its guesses, then the AUC score is 0.5. | |
C990 | Decision Tree - Classification. Decision tree builds classification or regression models in the form of a tree structure. It breaks down a dataset into smaller and smaller subsets while at the same time an associated decision tree is incrementally developed. | |
C991 | 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. | |
C992 | Some argue that such scores are at the ordinal level, providing only an ordering of performance. But, given the large number of potential values (95% of the population falls between 70 and 130 on an IQ scale), the scores function well as interval-scaled values. | |
C993 | Generally, standardized scores refer to raw data being converted to standard or normalized scores in order to maintain uniformity in interpretation of statistical data. Z scores are one of the most commonly used scores for data in statistics. They are also known as normal scores and standardized variables. | |
C994 | For a discrete random variable, the expected value, usually denoted as or , is calculated using: μ = E ( X ) = ∑ x i f ( x i ) | |
C995 | Generally speaking, gradient boosted trees are more robust in multicollinearity situations than OLS regression. When two independent variables are highly correlated, applying OLS regression could create problems. For example, p-values may not be reliable or even worse the OLS solution can't even be calculated. | |
C996 | In the literal meaning of the terms, a parametric statistical test is one that makes assumptions about the parameters (defining properties) of the population distribution(s) from which one's data are drawn, while a non-parametric test is one that makes no such assumptions. | |
C997 | This unit will calculate and/or estimate binomial probabilities for situations of the general "k out of n" type, where k is the number of times a binomial outcome is observed or stipulated to occur, p is the probability that the outcome will occur on any particular occasion, q is the complementary probability (1-p) | |
C998 | The simplest approach to identifying irregularities in data is to flag the data points that deviate from common statistical properties of a distribution, including mean, median, mode, and quantiles. Let's say the definition of an anomalous data point is one that deviates by a certain standard deviation from the mean. | |
C999 | Recurrent neural network works best for sequential data. |
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