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C7000 | FP. N. FN. TN. where: P = Positive; N = Negative; TP = True Positive; FP = False Positive; TN = True Negative; FN = False Negative. | |
C7001 | Normalization is a technique often applied as part of data preparation for machine learning. The goal of normalization is to change the values of numeric columns in the dataset to use a common scale, without distorting differences in the ranges of values or losing information. | |
C7002 | In neural networks, each neuron receives input from some number of locations in the previous layer. In a fully connected layer, each neuron receives input from every element of the previous layer. In a convolutional layer, neurons receive input from only a restricted subarea of the previous layer. | |
C7003 | An investor can calculate the coefficient of variation to help determine whether an investment's expected return is worth the volatility it is likely to experience over time. A lower ratio suggests a more favorable tradeoff between risk and return. | |
C7004 | 4:026:15Suggested clip · 93 secondsFinding the Test Statistic for a Wilcoxon Rank Sum Test in - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7005 | We can compute the p-value corresponding to the absolute value of the t-test statistics (|t|) for the degrees of freedom (df): df=n−1. If the p-value is inferior or equal to 0.05, we can conclude that the difference between the two paired samples are significantly different. | |
C7006 | Modified National Institute of Standards and Technology database | |
C7007 | If your learning rate is set too low, training will progress very slowly as you are making very tiny updates to the weights in your network. However, if your learning rate is set too high, it can cause undesirable divergent behavior in your loss function. | |
C7008 | A Poisson distribution is a measure of how many times an event is likely to occur within "X" period of time. Example: A video store averages 400 customers every Friday night. What is the probability that 600 customers will come in on any given Friday night? It was named after mathematician Siméon Denis Poisson. | |
C7009 | Optuna is an automated hyperparameter optimization software framework that is knowingly invented for the machine learning-based tasks. It emphasizes an authoritative, define-by-run approach user API. | |
C7010 | An N-point DFT is expressed as the multiplication , where is the original input signal, is the N-by-N square DFT matrix, and. is the DFT of the signal. | |
C7011 | Steps for Making decision treeGet list of rows (dataset) which are taken into consideration for making decision tree (recursively at each nodes).Calculate uncertanity of our dataset or Gini impurity or how much our data is mixed up etc.Generate list of all question which needs to be asked at that node.More items• | |
C7012 | In vector calculus and physics, a vector field is an assignment of a vector to each point in a subset of space. For instance, a vector field in the plane can be visualised as a collection of arrows with a given magnitude and direction, each attached to a point in the plane. | |
C7013 | Subtract the sample mean derived in the previous step from each of the data values, to get the deviation of each value from the sample mean. Multiply each deviation by itself to get the squared deviations of the values. Add up the squared deviations. | |
C7014 | Batch normalization works best after the activation function, and here or here is why: it was developed to prevent internal covariate shift. Internal covariate shift occurs when the distribution of the activations of a layer shifts significantly throughout training. | |
C7015 | 3.4. 1 The Logit Link Function. The logit link function is used to model the probability of 'success' as a function of covariates (e.g., logistic regression). | |
C7016 | Image processing is an important component of applications used in the publishing, satellite imagery analysis, medical, and seismic imaging fields. | |
C7017 | Neural networks work better at predictive analytics because of the hidden layers. Linear regression models use only input and output nodes to make predictions. Neural network also use the hidden layer to make predictions more accurate. That's because it 'learns' the way a human does. | |
C7018 | Elastic net is a popular type of regularized linear regression that combines two popular penalties, specifically the L1 and L2 penalty functions. Elastic Net is an extension of linear regression that adds regularization penalties to the loss function during training. | |
C7019 | How to Deal with MulticollinearityRemove some of the highly correlated independent variables.Linearly combine the independent variables, such as adding them together.Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression. | |
C7020 | A validation dataset is a dataset of examples used to tune the hyperparameters (i.e. the architecture) of a classifier. It is sometimes also called the development set or the "dev set". An example of a hyperparameter for artificial neural networks includes the number of hidden units in each layer. | |
C7021 | Without further ado and in no particular order, here are the top 5 machine learning algorithms for those just getting started:Linear regression. Logical regression. Classification and regression trees. K-nearest neighbor (KNN) Naïve Bayes. | |
C7022 | Heteroscedasticity means unequal scatter. In regression analysis, we talk about heteroscedasticity in the context of the residuals or error term. Specifically, heteroscedasticity is a systematic change in the spread of the residuals over the range of measured values. | |
C7023 | 1 : the quality or state of being reliable. 2 : the extent to which an experiment, test, or measuring procedure yields the same results on repeated trials. | |
C7024 | Deep learning techniques do not perform well when dealing with data with complex hierarchical structures. Deep learning identifies correlations between sets of features that are themselves “flat” or non-hierarchical, as in a simple, unstructured list, but much human and linguistic knowledge is more structured. | |
C7025 | A GLM is absolutely a statistical model, but statistical models and machine learning techniques are not mutually exclusive. In general, statistics is more concerned with inferring parameters, whereas in machine learning, prediction is the ultimate goal. | |
C7026 | When the membrane potential reaches the threshold, the neuron fires, and generates a signal that travels to other neurons which, in turn, increase or decrease their potentials in response to this signal. A neuron model that fires at the moment of threshold crossing is also called a spiking neuron model. | |
C7027 | Perceptron networks have several limitations. First, the output values of a perceptron can take on only one of two values (0 or 1) due to the hard-limit transfer function. Second, perceptrons can only classify linearly separable sets of vectors. | |
C7028 | Neural networks are widely used in unsupervised learning in order to learn better representations of the input data. This process doesn't give you clusters, but it creates meaningful representations that can be used for clustering. You could, for instance, run a clustering algorithm on the hidden layer's activations. | |
C7029 | There is currently no theoretical reason to use neural networks with any more than two hidden layers. In fact, for many practical problems, there is no reason to use any more than one hidden layer. Table 5.1 summarizes the capabilities of neural network architectures with various hidden layers. | |
C7030 | Answer. Answer: Explanation: Linear Discriminant Analysis (LDA) is most commonly used as dimensionality reduction technique in the pre-processing step for pattern-classification and machine learning applications. | |
C7031 | Gradient descent is a first-order iterative optimization algorithm for finding a local minimum of a differentiable function. To find a local minimum of a function using gradient descent, we take steps proportional to the negative of the gradient (or approximate gradient) of the function at the current point. | |
C7032 | Divide the number of events by the number of possible outcomes. This will give us the probability of a single event occurring. In the case of rolling a 3 on a die, the number of events is 1 (there's only a single 3 on each die), and the number of outcomes is 6. | |
C7033 | Hybrid Bayesian networks contain both discrete and continuous conditional probability distributions as numerical inputs. A commonly used type of hybrid Bayesian network is the conditional linear Gaussian (CLG) model [Lauritzen 1992, Cowell et al. | |
C7034 | These are generally used when direct sampling from the probability distribution would be difficult. Some of the use cases of MCMC methods are to approximate a target probability distribution or to compute an integral. | |
C7035 | Confirmation bias can make people less likely to engage with information which challenges their views. Even when people do get exposed to challenging information, confirmation bias can cause them to reject it and, perversely, become even more certain that their own beliefs are correct. | |
C7036 | If your p-value is less than or equal to the set significance level, the data is considered statistically significant. As a general rule, the significance level (or alpha) is commonly set to 0.05, meaning that the probability of observing the differences seen in your data by chance is just 5%. | |
C7037 | How to Find a Sample Size Given a Confidence Interval and Width (unknown population standard deviation)za/2: Divide the confidence interval by two, and look that area up in the z-table: .95 / 2 = 0.475. E (margin of error): Divide the given width by 2. 6% / 2. : use the given percentage. 41% = 0.41. : subtract. from 1. | |
C7038 | CHARACTERISTICS OF A LINEAR MODELIt is a model, in which something progresses or develops directly from one stage to another.A linear model is known as a very direct model, with starting point and ending point.Linear model progresses to a sort of pattern with stages completed one after another without going back to prior phases.More items• | |
C7039 | Information theory studies the quantification, storage, and communication of information. It was originally proposed by Claude Shannon in 1948 to find fundamental limits on signal processing and communication operations such as data compression, in a landmark paper titled "A Mathematical Theory of Communication". | |
C7040 | A false positive means that the results say you have the condition you were tested for, but you really don't. With a false negative, the results say you don't have a condition, but you really do. | |
C7041 | The four requirements are: each observation falls into one of two categories called a success or failure. there is a fixed number of observations. the observations are all independent. the probability of success (p) for each observation is the same - equally likely. | |
C7042 | DCGAN is one of the popular and successful network design for GAN. It mainly composes of convolution layers without max pooling or fully connected layers. It uses convolutional stride and transposed convolution for the downsampling and the upsampling. The figure below is the network design for the generator. Source. | |
C7043 | A term document matrix is a way of representing the words in the text as a table (or matrix) of numbers. The rows of the matrix represent the text responses to be analysed, and the columns of the matrix represent the words from the text that are to be used in the analysis. The most basic version is binary. | |
C7044 | The limitation of Kaplan Meier estimate is that it cannot be used for multivariate analysis as it only studies the effect of one factor at the time. Log-rank test is used to compare two or more groups by testing the null hypothesis. | |
C7045 | Simple random samples involve the random selection of data from the entire population so each possible sample is equally likely to occur. In contrast, stratified random sampling divides the population into smaller groups, or strata, based on shared characteristics. | |
C7046 | Temporal Difference is an approach to learning how to predict a quantity that depends on future values of a given signal. It can be used to learn both the V-function and the Q-function, whereas Q-learning is a specific TD algorithm used to learn the Q-function. | |
C7047 | Kalman filters are used to optimally estimate the variables of interests when they can't be measured directly, but an indirect measurement is available. They are also used to find the best estimate of states by combining measurements from various sensors in the presence of noise. | |
C7048 | 1. Interactions in Multiple Linear Regression. Basic Ideas. Interaction: An interaction occurs when an independent variable has a different effect on the outcome depending on the values of another independent variable. Let's look at some examples. | |
C7049 | The general linear model requires that the response variable follows the normal distribution whilst the generalized linear model is an extension of the general linear model that allows the specification of models whose response variable follows different distributions. | |
C7050 | Platt Scaling is most effective when the distortion in the predicted probabilities is sigmoid shaped. Isotonic Regression is a more powerful calibration method that can correct any monotonic distortion. But, Isotonic Regression is prone to over-fitting. | |
C7051 | The function fX(x) gives us the probability density at point x. It is the limit of the probability of the interval (x,x+Δ] divided by the length of the interval as the length of the interval goes to 0. | |
C7052 | Generally, we use linear regression for time series analysis, it is used for predicting the result for time series as its trends. For example, If we have a dataset of time series with the help of linear regression we can predict the sales with the time. | |
C7053 | The technological singularity—also, simply, the singularity—is a hypothetical point in time at which technological growth becomes uncontrollable and irreversible, resulting in unforeseeable changes to human civilization. | |
C7054 | Precision and recall both have true positives in the numerator, and different denominators. To average them it really only makes sense to average their reciprocals, thus the harmonic mean. Because it punishes extreme values more. With the harmonic mean, the F1-measure is 0. | |
C7055 | In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. | |
C7056 | Predictive modeling is the subpart of data analytics that uses data mining and probability to predict results. Each model is built up by the number of predictors that are highly favorable to determine future decisions. Once the data is received for a specific predictor, an analytical model is formulated. | |
C7057 | Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it's not the amount of data that's important. Big data can be analyzed for insights that lead to better decisions and strategic business moves. | |
C7058 | Probability theory is the mathematical framework that allows us to analyze chance events in a logically sound manner. The probability of an event is a number indicating how likely that event will occur. This number is always between 0 and 1, where 0 indicates impossibility and 1 indicates certainty. | |
C7059 | The Beta distribution is a continuous probability distribution having two parameters. One of its most common uses is to model one's uncertainty about the probability of success of an experiment. | |
C7060 | Multinomial logistic regression deals with situations where the outcome can have three or more possible types (e.g., "disease A" vs. "disease B" vs. "disease C") that are not ordered. Binary logistic regression is used to predict the odds of being a case based on the values of the independent variables (predictors). | |
C7061 | In mathematics, a Fourier transform (FT) is a mathematical transform that decomposes a function (often a function of time, or a signal) into its constituent frequencies, such as the expression of a musical chord in terms of the volumes and frequencies of its constituent notes. | |
C7062 | The minimum description length (MDL) principle is a powerful method of inductive inference, the basis of statistical modeling, pattern recognition, and machine learning. It holds that the best explanation, given a limited set of observed data, is the one that permits the greatest compression of the data. | |
C7063 | 0:0411:02Suggested clip · 75 secondsControl variables in regression - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7064 | A data point is a discrete unit of information. In a general sense, any single fact is a data point. In a statistical or analytical context, a data point is usually derived from a measurement or research and can be represented numerically and/or graphically. | |
C7065 | A regression equation is used in stats to find out what relationship, if any, exists between sets of data. For example, if you measure a child's height every year you might find that they grow about 3 inches a year. That trend (growing three inches a year) can be modeled with a regression equation. | |
C7066 | Univariate statistics summarize only one variable at a time. Bivariate statistics compare two variables. Multivariate statistics compare more than two variables. | |
C7067 | The decision rule is: Reject H0 if Z > 1.645. The decision rule is: Reject H0 if Z < 1.645. The decision rule is: Reject H0 if Z < -1.960 or if Z > 1.960. The complete table of critical values of Z for upper, lower and two-tailed tests can be found in the table of Z values to the right in "Other Resources." | |
C7068 | Anchor boxes eliminate the need to scan an image with a sliding window that computes a separate prediction at every potential position. Because a convolutional neural network (CNN) can process an input image in a convolutional manner, a spatial location in the input can be related to a spatial location in the output. | |
C7069 | The Rabin-Karp algorithm makes use of hash functions and the rolling hash technique. A hash function is essentially a function that maps one thing to a value. In particular, hashing can map data of arbitrary size to a value of fixed size. | |
C7070 | 0:112:51Suggested clip · 118 secondsMath for Liberal Studies: Using the Nearest-Neighbor Algorithm YouTubeStart of suggested clipEnd of suggested clip | |
C7071 | The statistic used to estimate the mean of a population, μ, is the sample mean, . If X has a distribution with mean μ, and standard deviation σ, and is approximately normally distributed or n is large, then is approximately normally distributed with mean μ and standard error .. | |
C7072 | Regression and classification are categorized under the same umbrella of supervised machine learning. The main difference between them is that the output variable in regression is numerical (or continuous) while that for classification is categorical (or discrete). | |
C7073 | Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign, or to distinguish a pedestrian from a lamppost. | |
C7074 | Explanation: If a bayesian network is a representation of the joint distribution, then it can solve any query, by summing all the relevant joint entries. | |
C7075 | On each edge there are only states moving in one direction, and the direction is opposite for opposite edges. These strange states obtained at the edges are often referred to as chiral edge states. The chirality of the edges is determined by the orientation of the magnetic field (out of the plane vs. | |
C7076 | There is a direct relationship between the coefficients produced by logit and the odds ratios produced by logistic. First, let's define what is meant by a logit: A logit is defined as the log base e (log) of the odds. : [1] logit(p) = log(odds) = log(p/q) The range is negative infinity to positive infinity. | |
C7077 | Mutual information is one of many quantities that measures how much one random variables tells us about another. It is a dimensionless quantity with (generally) units of bits, and can be thought of as the reduction in uncertainty about one random variable given knowledge of another. | |
C7078 | The (BIG) Z is a similar to the small z for one very good reason: It is a standard score. The z score has the sample standard deviation as the denominator, whereas the z-test value has the standard error of the mean ( or a measure of the variability of all the means from the population) as the dominator. | |
C7079 | Discriminant function analysis (DFA) is a statistical procedure that classifies unknown individuals and the probability of their classification into a certain group (such as sex or ancestry group). Discriminant function analysis makes the assumption that the sample is normally distributed for the trait. | |
C7080 | Multilevel models are particularly appropriate for research designs where data for participants are organized at more than one level (i.e., nested data). The units of analysis are usually individuals (at a lower level) who are nested within contextual/aggregate units (at a higher level). | |
C7081 | In probability theory and statistics, the exponential distribution is the probability distribution of the time between events in a Poisson point process, i.e., a process in which events occur continuously and independently at a constant average rate. | |
C7082 | 0:255:16Suggested clip · 118 secondsConvolution of Two Functions - YouTubeYouTubeStart of suggested clipEnd of suggested clip | |
C7083 | n = norm( X ) returns the 2-norm or maximum singular value of matrix X , which is approximately max(svd(X)) . n = norm( X , p ) returns the p-norm of matrix X , where p is 1 , 2 , or Inf : If p = 1 , then n is the maximum absolute column sum of the matrix. If p = 2 , then n is approximately max(svd(X)) . | |
C7084 | The cumulative distribution function (cdf) of a continuous random variable X is defined in exactly the same way as the cdf of a discrete random variable. F (b) = P (X ≤ b). F (b) = P (X ≤ b) = f(x) dx, where f(x) is the pdf of X. | |
C7085 | Linear filtering is the filtering method in which the value of output pixel is linear combinations of the neighbouring input pixels. A non-linear filtering is one that cannot be done with convolution or Fourier multiplication. A sliding median filter is a simple example of a non-linear filter. | |
C7086 | The normal approximation to the binomial is when you use a continuous distribution (the normal distribution) to approximate a discrete distribution (the binomial distribution). | |
C7087 | Decision Tree - Overfitting There are several approaches to avoiding overfitting in building decision trees. Pre-pruning that stop growing the tree earlier, before it perfectly classifies the training set. Post-pruning that allows the tree to perfectly classify the training set, and then post prune the tree. | |
C7088 | The Poisson Distribution formula is: P(x; μ) = (e-μ) (μx) / x! Let's say that that x (as in the prime counting function is a very big number, like x = 10100. If you choose a random number that's less than or equal to x, the probability of that number being prime is about 0.43 percent. | |
C7089 | Signal processing is essential for the use of X-rays, MRIs and CT scans, allowing medical images to be analyzed and deciphered by complex data processing techniques. Signals are used in finance, to send messages about and interpret financial data. This aids decision-making in trading and building stock portfolios. | |
C7090 | Absolutely, depth refers to the number of layers whereas receptive field size is specific to ConvNets and refers to the portion of the original input that a layer can see. See here: What is a receptive field in a convolutional neural network? How do I learn convolutional neural network theory? | |
C7091 | In short, fourier series is for periodic signals and fourier transform is for aperiodic signals. Fourier series is used to decompose signals into basis elements (complex exponentials) while fourier transforms are used to analyze signal in another domain (e.g. from time to frequency, or vice versa). | |
C7092 | KNN works by finding the distances between a query and all the examples in the data, selecting the specified number examples (K) closest to the query, then votes for the most frequent label (in the case of classification) or averages the labels (in the case of regression). | |
C7093 | The number of hidden neurons should be between the size of the input layer and the size of the output layer. The number of hidden neurons should be 2/3 the size of the input layer, plus the size of the output layer. | |
C7094 | We can use MLE in order to get more robust parameter estimates. Thus, MLE can be defined as a method for estimating population parameters (such as the mean and variance for Normal, rate (lambda) for Poisson, etc.) from sample data such that the probability (likelihood) of obtaining the observed data is maximized. | |
C7095 | A modern approach to reducing generalization error is to use a larger model that may be required to use regularization during training that keeps the weights of the model small. These techniques not only reduce overfitting, but they can also lead to faster optimization of the model and better overall performance. | |
C7096 | In cluster sampling, researchers divide a population into smaller groups known as clusters. They then randomly select among these clusters to form a sample. Cluster sampling is a method of probability sampling that is often used to study large populations, particularly those that are widely geographically dispersed. | |
C7097 | x = A ./ B divides each element of A by the corresponding element of B . The sizes of A and B must be the same or be compatible. If the sizes of A and B are compatible, then the two arrays implicitly expand to match each other. | |
C7098 | A probability distribution is a list of outcomes and their associated probabilities. A function that represents a discrete probability distribution is called a probability mass function. A function that represents a continuous probability distribution is called a probability density function. | |
C7099 | Cross-entropy loss, or log loss, measures the performance of a classification model whose output is a probability value between 0 and 1. Cross-entropy loss increases as the predicted probability diverges from the actual label. |
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