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C3800 | A random variable is a variable that takes specific values with specific probabilities. It can be thought of as a variable whose value depends on the outcome of an uncertain event. 2. We usually denote random variables by capital letters near the end of the alphabet; e.g., X,Y,Z. | |
C3801 | In statistics, a positively skewed (or right-skewed) distribution is a type of distribution in which most values are clustered around the left tail of the distribution while the right tail of the distribution is longer. | |
C3802 | A sampling frame is a list or other device used to define a researcher's population of interest. The sampling frame defines a set of elements from which a researcher can select a sample of the target population. Comprehensiveness refers to the degree to which a sampling frame covers the entire target population. | |
C3803 | Simple linear regression is a statistical method that allows us to summarize and study relationships between two continuous (quantitative) variables: One variable, denoted x, is regarded as the predictor, explanatory, or independent variable. | |
C3804 | If you are working on a classification problem, the best score is 100% accuracy. If you are working on a regression problem, the best score is 0.0 error. These scores are an impossible to achieve upper/lower bound. All predictive modeling problems have prediction error. | |
C3805 | Definition of 'average deviation' 1. the difference between an observed value of a variable and its mean. 2. Also: mean deviation from the mean, mean deviation from the median, average deviation. | |
C3806 | Bayesian neural networks differ from plain neural networks in that their weights are assigned a probability distribution instead of a single value or point estimate. These probability distributions describe the uncertainty in weights and can be used to estimate uncertainty in predictions. | |
C3807 | Simpson's paradox can be avoided by selecting an appropriate experimental design and analysis that incorporates the confounding variable in such a way as to obtain unconfounded estimates of treatment effects, thus more accurately answering the research question. | |
C3808 | Random Walk states that stock prices cannot be reliably predicted. In the EMH, prices reflect all the relevant information regarding a financial asset; while in Random Walk, prices literally take a 'random walk' and can even be influenced by 'irrelevant' information. | |
C3809 | In mathematics, measurement typically refers to understanding units and precision in problems that deal with most concrete measures such as length, area, and volume. But, in statistics, measurement can be a bit more abstract. Statistics, however, utilizes inductive reasoning and conclusions are always uncertain. | |
C3810 | Let's GO!Step 0 : Pre-requisites. It is recommended that before jumping on to Deep Learning, you should know the basics of Machine Learning. Step 1 : Setup your Machine. Step 2 : A Shallow Dive. Step 3 : Choose your own Adventure! Step 4 : Deep Dive into Deep Learning. | |
C3811 | The binomial is a type of distribution that has two possible outcomes (the prefix “bi” means two, or twice). For example, a coin toss has only two possible outcomes: heads or tails and taking a test could have two possible outcomes: pass or fail. A Binomial Distribution shows either (S)uccess or (F)ailure. | |
C3812 | Eight Steps on How to Reduce Bias in AIDefine and narrow the business problem you're solving. Structure data gathering that allows for different opinions. Understand your training data. Gather a diverse ML team that asks diverse questions. Think about all of your end-users. Annotate with diversity.More items• | |
C3813 | Interpolation refers to using the data in order to predict data within the dataset. Extrapolation is the use of the data set to predict beyond the data set. | |
C3814 | Bootstrap aggregating, also called bagging (from bootstrap aggregating), is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy of machine learning algorithms used in statistical classification and regression. It also reduces variance and helps to avoid overfitting. | |
C3815 | Definition: Let X be a continuous random variable with mean µ. The variance of X is Var(X) = E((X − µ)2). | |
C3816 | AdaBoost. AdaBoost is an ensemble machine learning algorithm for classification problems. It is part of a group of ensemble methods called boosting, that add new machine learning models in a series where subsequent models attempt to fix the prediction errors made by prior models. | |
C3817 | Backpropagation only works during training the model on a dataset. You run your model with the learned parameters (from Backpropagation) and best hyperparameters (from validation) once on the Test set and report the accuracy. You never learn anything, be it parameters or hyperparameters on the Test set. | |
C3818 | Hysteresis is the difference between two separate measurements taken at the same point, the first is taken during a series of increasing measurement values, and the other during during a series of decreasing measurement values. | |
C3819 | Inverted dropout is a variant of the original dropout technique developed by Hinton et al. Just like traditional dropout, inverted dropout randomly keeps some weights and sets others to zero. In contrast, traditional dropout requires scaling to be implemented during the test phase. | |
C3820 | The principal advantage of linear regression is its simplicity, interpretability, scientific acceptance, and widespread availability. Linear regression is the first method to use for many problems. | |
C3821 | Improved response times: Data visualization puts the data into the users' hands allowing them to more quickly identify issues and improve response times. It allows decision-makers to view data using graphical representations including charts, fever charts, and heat maps. | |
C3822 | Two types of reinforcement learning are 1) Positive 2) Negative. Two widely used learning model are 1) Markov Decision Process 2) Q learning. Reinforcement Learning method works on interacting with the environment, whereas the supervised learning method works on given sample data or example. | |
C3823 | Given a linear operator it can have associated eigenvectors / functions. For example, given the differential operator the exponential function is an eigenfunction of it. This is why we can solve linear homogeneous differential equations by solving a characteristic equation. | |
C3824 | However, people generally apply this probability to a single study. Consequently, an odds ratio of 5.2 with a confidence interval of 3.2 to 7.2 suggests that there is a 95% probability that the true odds ratio would be likely to lie in the range 3.2-7.2 assuming there is no bias or confounding. | |
C3825 | An ARIMA model is a class of statistical models for analyzing and forecasting time series data. The use of differencing of raw observations (e.g. subtracting an observation from an observation at the previous time step) in order to make the time series stationary. MA: Moving Average. | |
C3826 | Keras is a neural network library while TensorFlow is the open-source library for a number of various tasks in machine learning. TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs. | |
C3827 | A latent variable is a variable that cannot be observed. The presence of latent variables, however, can be detected by their effects on variables that are observable. Most constructs in research are latent variables. Consider the psychological construct of anxiety, for example. | |
C3828 | four outcomes | |
C3829 | A significant advantage of a decision tree is that it forces the consideration of all possible outcomes of a decision and traces each path to a conclusion. It creates a comprehensive analysis of the consequences along each branch and identifies decision nodes that need further analysis. | |
C3830 | The bootstrap method is a resampling technique used to estimate statistics on a population by sampling a dataset with replacement. It is used in applied machine learning to estimate the skill of machine learning models when making predictions on data not included in the training data. | |
C3831 | • Parametric tests are based on assumptions about the distribution of the underlying. population from which the sample was taken. The most common parametric. assumption is that data are approximately normally distributed. • Nonparametric tests do not rely on assumptions about the shape or parameters of the. | |
C3832 | This is a form of regression, that constrains/ regularizes or shrinks the coefficient estimates towards zero. In other words, this technique discourages learning a more complex or flexible model, so as to avoid the risk of overfitting. A simple relation for linear regression looks like this. | |
C3833 | The binomial distribution model allows us to compute the probability of observing a specified number of "successes" when the process is repeated a specific number of times (e.g., in a set of patients) and the outcome for a given patient is either a success or a failure. | |
C3834 | A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. Supervised learning uses classification and regression techniques to develop predictive models. | |
C3835 | Linear regression is the next step up after correlation. It is used when we want to predict the value of a variable based on the value of another variable. The variable we want to predict is called the dependent variable (or sometimes, the outcome variable). | |
C3836 | Why Backing Up is Essential: The Top Five Benefits to Data BackupQuick Access to Files. One of the greatest things about backing up data is the ease at which you are able to retrieve files and information. Protection Against Power Failures. Added Anti-Virus Protection. Safeguard Against Failed Hard Drive. Recovery if Operating System Fails. | |
C3837 | The answer is whitening. If x is an n dimensional column vector of zero mean and has an n by n covariance R then x' inv(R) x is chi squared, that is x' inv(R) x is the sum of n unit variance zero mean random variables. | |
C3838 | The k-nearest neighbors (KNN) algorithm is a simple, easy-to-implement supervised machine learning algorithm that can be used to solve both classification and regression problems. | |
C3839 | Word2Vec can be used to get actionable metrics from thousands of customers reviews. Businesses don't have enough time and tools to analyze survey responses and act on them thereon. This leads to loss of ROI and brand value. Word embeddings prove invaluable in such cases. | |
C3840 | Symmetrical distribution occurs when the values of variables occur at regular frequencies and the mean, median and mode occur at the same point. In graph form, symmetrical distribution often appears as a bell curve. If a line were drawn dissecting the middle of the graph, it would show two sides that mirror each other. | |
C3841 | As the df increase, the chi square distribution approaches a normal distribution. The mean of a chi square distribution is its df. The mode is df - 2 and the median is approximately df - 0 . | |
C3842 | CNNs are trained to identify and extract the best features from the images for the problem at hand. That is their main strength. The latter layers of a CNN are fully connected because of their strength as a classifier. | |
C3843 | Ensemble learning helps improve machine learning results by combining several models. This approach allows the production of better predictive performance compared to a single model. | |
C3844 | A simple random sample is a subset of a statistical population in which each member of the subset has an equal probability of being chosen. A simple random sample is meant to be an unbiased representation of a group. | |
C3845 | Gradient boosting is a machine learning technique for regression and classification problems, which produces a prediction model in the form of an ensemble of weak prediction models, typically decision trees. | |
C3846 | semi-supervised learning model | |
C3847 | Prior probability represents what is originally believed before new evidence is introduced, and posterior probability takes this new information into account. A posterior probability can subsequently become a prior for a new updated posterior probability as new information arises and is incorporated into the analysis. | |
C3848 | Boosting is a general ensemble method that creates a strong classifier from a number of weak classifiers. This is done by building a model from the training data, then creating a second model that attempts to correct the errors from the first model. It is the best starting point for understanding boosting. | |
C3849 | Response variables are also known as dependent variables, y-variables, and outcome variables. Typically, you want to determine whether changes in the predictors are associated with changes in the response. For example, in a plant growth study, the response variable is the amount of growth that occurs during the study. | |
C3850 | The tobit model, also called a censored regression model, is designed to estimate linear relationships between variables when there is either left- or right-censoring in the dependent variable (also known as censoring from below and above, respectively). | |
C3851 | Generally, the rule of thumb is that the larger the sample size, the more statistically significant it is—meaning there's less of a chance that your results happened by coincidence. | |
C3852 | Tensorflow is the most popular and apparently best Deep Learning Framework out there. Tensorflow can be used to achieve all of these applications. The reason for its popularity is the ease with which developers can build and deploy applications. | |
C3853 | A post hoc test is used only after we find a statistically significant result and need to determine where our differences truly came from. The term “post hoc” comes from the Latin for “after the event”. There are many different post hoc tests that have been developed, and most of them will give us similar answers. | |
C3854 | Categorization is a major component of qualitative data analysis by which investigators attempt to group patterns observed in the data into meaningful units or categories. Through this process, categories are often created by chunking together groups of previously coded data. | |
C3855 | While measures of central tendency are used to estimate "normal" values of a dataset, measures of dispersion are important for describing the spread of the data, or its variation around a central value. A proper description of a set of data should include both of these characteristics. | |
C3856 | A probability distribution is a statistical function that describes all the possible values and likelihoods that a random variable can take within a given range. These factors include the distribution's mean (average), standard deviation, skewness, and kurtosis. | |
C3857 | In the terminology of machine learning, classification is considered an instance of supervised learning, i.e., learning where a training set of correctly identified observations is available. An algorithm that implements classification, especially in a concrete implementation, is known as a classifier. | |
C3858 | A polygon is convex if all the interior angles are less than 180 degrees. If one or more of the interior angles is more than 180 degrees the polygon is non-convex (or concave). | |
C3859 | In this paper we describe a multiagent Q-learning tech- nique, called Sparse Cooperative Q-learning, that al- lows a group of agents to learn how to jointly solve a task when the global coordination requirements of the system (but not the particular action choices of the agents) are known beforehand. | |
C3860 | The Four Assumptions of Linear RegressionLinear relationship: There exists a linear relationship between the independent variable, x, and the dependent variable, y.Independence: The residuals are independent. Homoscedasticity: The residuals have constant variance at every level of x.Normality: The residuals of the model are normally distributed. | |
C3861 | A square matrix that is not invertible is called singular or degenerate. A square matrix is singular if and only if its determinant is zero. Non-square matrices (m-by-n matrices for which m ≠ n) do not have an inverse. However, in some cases such a matrix may have a left inverse or right inverse. | |
C3862 | Euclidean distance | |
C3863 | The probability of making a type I error is α, which is the level of significance you set for your hypothesis test. An α of 0.05 indicates that you are willing to accept a 5% chance that you are wrong when you reject the null hypothesis. The probability of rejecting the null hypothesis when it is false is equal to 1–β. | |
C3864 | For high-dimensional datasets (i.e. with number of dimensions more than 10), dimension reduction is usually performed prior to applying a K-nearest neighbors algorithm (k-NN) in order to avoid the effects of the curse of dimensionality. | |
C3865 | We use three main types of layers to build ConvNet architectures: Convolutional Layer, Pooling Layer, and Fully-Connected Layer (exactly as seen in regular Neural Networks). | |
C3866 | Gloves are pieces of clothing which cover your hands and wrists and have individual sections for each finger. You wear gloves to keep your hands warm or dry or to protect them. a pair of white cotton gloves. Synonyms: mitten, gauntlet, mitt More Synonyms of glove. 2. | |
C3867 | Most recent answer. Three different measures of effect size for chi-squared test and Fisher's exact test predominantly used are Phi, Cramer's V, and Odds Ratio. Phi and Odds Ratio are only suitable for a 2x2 contingency table and Cramer's V is suitable for larger contingency tables. | |
C3868 | To calculate the Sharpe Ratio, find the average of the “Portfolio Returns (%)” column using the “=AVERAGE” formula and subtract the risk-free rate out of it. Divide this value by the standard deviation of the portfolio returns, which can be found using the “=STDEV” formula. | |
C3869 | A theorem that explains the shape of a sampling distribution of sample means. It states that if the sample size is large (generally n ≥ 30), and the standard deviation of the population is finite, then the distribution of sample means will be approximately normal. | |
C3870 | Gradient boosting is a type of machine learning boosting. It relies on the intuition that the best possible next model, when combined with previous models, minimizes the overall prediction error. The key idea is to set the target outcomes for this next model in order to minimize the error. | |
C3871 | The beta distribution is a continuous probability distribution that can be used to represent proportion or probability outcomes. For example, the beta distribution might be used to find how likely it is that your preferred candidate for mayor will receive 70% of the vote. | |
C3872 | Active learning promotes recall and deeper understanding of material, as students are engaging with the content rather than simply listening to it. It helps to maintain student concentration and deepens learning towards the higher-level skills like critical thinking. | |
C3873 | Multivariate data is a set of data with more than two variables per observation. Multidimensional databases contain fact tables (with observations, events, or transactions, say) and dimension tables, which more fully describe or put into context the variables in the fact tables — such as time hierarchies. | |
C3874 | In qualitative research, there are various sampling techniques that you can use when recruiting participants. The two most popular sampling techniques are purposeful and convenience sampling because they align the best across nearly all qualitative research designs. | |
C3875 | q is called the variational approximation to the posterior. The term variational is used because you pick the best q in Q -- the term derives from the "calculus of variations," which deals with optimization problems that pick the best function (in this case, a distribution q). | |
C3876 | Rabin-Karp is another pattern searching algorithm to find the pattern in a more efficient way. It also checks the pattern by moving window one by one, but without checking all characters for all cases, it finds the hash value. When the hash value is matched, then only it tries to check each character. | |
C3877 | Introduction. Linear regression and logistic regression are two types of regression analysis techniques that are used to solve the regression problem using machine learning. They are the most prominent techniques of regression. | |
C3878 | An algorithm that uses random numbers to decide what to do next anywhere in its logic is called a Randomized Algorithm. For example, in Randomized Quick Sort, we use a random number to pick the next pivot (or we randomly shuffle the array). And in Karger's algorithm, we randomly pick an edge. | |
C3879 | Categorical variables are also known as discrete or qualitative variables. Categorical variables can be further categorized as either nominal, ordinal or dichotomous. Nominal variables are variables that have two or more categories, but which do not have an intrinsic order. | |
C3880 | To overcome this prob- lem, the ResNet incorporates skip-connections between layers (He et al., 2016a,b) and the batch-normalization (BN) normalizes the input of activation functions (Ioffe and Szegedy, 2015). These architectures enable an extreme deep neural network to be trained with high performance. | |
C3881 | Sampling is a process used in statistical analysis in which a predetermined number of observations are taken from a larger population. The methodology used to sample from a larger population depends on the type of analysis being performed, but it may include simple random sampling or systematic sampling. | |
C3882 | How to Detect Omitted Variable Bias and Identify Confounding Variables. You saw one method of detecting omitted variable bias in this post. If you include different combinations of independent variables in the model, and you see the coefficients changing, you're watching omitted variable bias in action! | |
C3883 | MATLAB is a high-performance language for technical computing. It integrates computation, visualization, and programming in an easy-to-use environment where problems and solutions are expressed in familiar mathematical notation. Typical uses include: Data analysis, exploration, and visualization. | |
C3884 | K nearest neighbors is a simple algorithm that stores all available cases and classifies new cases based on a similarity measure (e.g., distance functions). KNN has been used in statistical estimation and pattern recognition already in the beginning of 1970's as a non-parametric technique. | |
C3885 | 0:0116:09Suggested clip · 82 secondsAnalyzing Models with TensorBoard - Deep Learning with Python YouTubeStart of suggested clipEnd of suggested clip | |
C3886 | This is machine learning in general and almost all ML algorithms are based on this optimization. Curve fitting, on the other hand, is a process of finding a mathematical function on the available data such that the function defines the best fit on the data points. ML does the same but it needs to generalize it's fit. | |
C3887 | An endogenous variable is a variable in a statistical model that's changed or determined by its relationship with other variables within the model. Therefore, its values may be determined by other variables. Endogenous variables are the opposite of exogenous variables, which are independent variables or outside forces. | |
C3888 | Object recognition is a computer vision technique for identifying objects in images or videos. Object recognition is a key output of deep learning and machine learning algorithms. The goal is to teach a computer to do what comes naturally to humans: to gain a level of understanding of what an image contains. | |
C3889 | In machine learning and mathematical optimization, loss functions for classification are computationally feasible loss functions representing the price paid for inaccuracy of predictions in classification problems (problems of identifying which category a particular observation belongs to). | |
C3890 | The least squares approach limits the distance between a function and the data points that the function explains. It is used in regression analysis, often in nonlinear regression modeling in which a curve is fit into a set of data. Mathematicians use the least squares method to arrive at a maximum-likelihood estimate. | |
C3891 | 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 ). | |
C3892 | Image processing is a method to perform some operations on an image, in order to get an enhanced image or to extract some useful information from it. It is a type of signal processing in which input is an image and output may be image or characteristics/features associated with that image. | |
C3893 | The Fourier transform of a function of time is a complex-valued function of frequency, whose magnitude (absolute value) represents the amount of that frequency present in the original function, and whose argument is the phase offset of the basic sinusoid in that frequency. | |
C3894 | Because the distance function used to find the k nearest neighbors is not linear, so it usually won't lead to a linear decision boundary. kNN does not build a model of your data, it simply assumes that instances that are close together in space are similar. | |
C3895 | Moments About the MeanFirst, calculate the mean of the values.Next, subtract this mean from each value.Then raise each of these differences to the sth power.Now add the numbers from step #3 together.Finally, divide this sum by the number of values we started with. | |
C3896 | Tensors are a type of data structure used in linear algebra, and like vectors and matrices, you can calculate arithmetic operations with tensors. | |
C3897 | There are four possible outcomes: hit (signal present and subject says “yes”), miss (signal present and subject says “no”), false alarm (signal absent and subject says “yes”), and correct rejection (signal absent and subject says “no”). Hits and correct rejections are good. | |
C3898 | According to his theory, S-Shaped curved lines signify liveliness and activity and excite the attention of the viewer as contrasted with straight lines, parallel lines, or right-angled intersecting lines which signify stasis, death, or inanimate objects. He goes on to say that the S curve is the basis of all great art. | |
C3899 | A mathematical function with symbol εijk defined to switch between the discrete values of +1, 0, and -1, depending on the values of the three indices i, j, and k: It is one of the tools used in Einstein's summation notation to handle operations equivalent to cross products in vector notation. |
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